diff --git a/.gitignore b/.gitignore
index e8e5857..14908ae 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,10 +1,12 @@
 # IDEs
 .idea/
 .vscode/
+*.DS_Store
 
 # Python
 __pycache__/
 venv/
+venv_win/
 
 # Logging
 logs/
@@ -12,3 +14,12 @@ logs/
 # Pytorch and Ultralytics
 *.pt
 *.pth
+
+# Model blobs (large binaries, download via scripts/download_model.py)
+models/
+
+# Cache
+.cache/
+
+# Context/prompt files
+*.txt
\ No newline at end of file
diff --git a/accuracy_log.csv b/accuracy_log.csv
new file mode 100644
index 0000000..5f2ab41
--- /dev/null
+++ b/accuracy_log.csv
@@ -0,0 +1,951 @@
+timestamp,test_distance,target_id,x_mm,y_mm,z_mm
+
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diff --git a/config.yaml b/config.yaml
index 4b0d967..ba6a101 100644
--- a/config.yaml
+++ b/config.yaml
@@ -86,3 +86,13 @@ communications:
 
 auto_landing:
     enabled: true
+
+spatial_detection:
+    # Luxonis model zoo name. Auto-downloaded on first run, cached after.
+    model_name: "yolov6-nano"
+    confidence_threshold: 0.5
+    depth_lower_mm: 100    # Ignore detections closer than this (mm)
+    depth_upper_mm: 10000  # Ignore detections farther than this (mm)
+
+object_tracker:
+    target_class_id: 0  # COCO class 0 = person
diff --git a/documentation/accuracy/accuracy_log_2026-04-30.csv b/documentation/accuracy/accuracy_log_2026-04-30.csv
new file mode 100644
index 0000000..c90c71c
--- /dev/null
+++ b/documentation/accuracy/accuracy_log_2026-04-30.csv
@@ -0,0 +1,773 @@
+timestamp,test_distance,target_id,x_mm,y_mm,z_mm
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diff --git a/documentation/accuracy/accuracy_log_2026-05-07.csv b/documentation/accuracy/accuracy_log_2026-05-07.csv
new file mode 100644
index 0000000..9c9bfa1
--- /dev/null
+++ b/documentation/accuracy/accuracy_log_2026-05-07.csv
@@ -0,0 +1,841 @@
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diff --git a/documentation/accuracy/calibration.png b/documentation/accuracy/calibration.png
new file mode 100644
index 0000000..3219712
Binary files /dev/null and b/documentation/accuracy/calibration.png differ
diff --git a/graph.py b/graph.py
new file mode 100644
index 0000000..26aacad
--- /dev/null
+++ b/graph.py
@@ -0,0 +1,98 @@
+"""
+Generates z-accuracy graphs from accuracy_log.csv.
+Shows raw z vs nominal per distance.
+Run: python3 graph.py
+"""
+
+import pathlib
+
+import matplotlib.pyplot as plt
+import pandas as pd
+
+from main_2025 import calibrate_z
+
+REPO_DIR = pathlib.Path(__file__).parent
+LOG_FILE = REPO_DIR / "accuracy_log.csv"
+OUT_DIR = REPO_DIR / "graphs"
+
+NOMINAL_MM = {
+    "0.5m": 500,
+    "1.0m": 1000,
+    "1.5m": 1500,
+    "2.0m": 2000,
+    "2.5m": 2500,
+    "3.0m": 3000,
+}
+
+COLORS = ["tab:orange", "tab:blue", "tab:green", "tab:red", "tab:purple"]
+
+Y_MARGIN_MM = 150  # fixed ±margin around nominal for consistent scale across all panels
+Y_LIMITS = {
+    "0.5m": (250, 750),  # nominal ±250 — keeps the 500mm nominal line centered
+    "2.0m": (1800, 2200),
+    "3.0m": (2700, 3300),
+}  # per-distance overrides
+
+
+def plot_distance(ax, dist, raw_z, nominal, color):
+    """Plot a single distance panel — usual pipeline calibration applied."""
+    z = raw_z.apply(calibrate_z)
+    mean_z = z.mean()
+    ax.plot(
+        z.index, z, color=color, linewidth=0.8, alpha=0.85, label=f"measured z (μ={mean_z:.0f}mm)"
+    )
+    if nominal:
+        ax.axhline(
+            nominal, color="red", linestyle="--", linewidth=1.2, label=f"nominal ({nominal}mm)"
+        )
+    ax.axhline(mean_z, color="gray", linestyle=":", linewidth=1.2, label=f"mean ({mean_z:.0f}mm)")
+    ymin, ymax = Y_LIMITS.get(dist, (nominal - Y_MARGIN_MM, nominal + Y_MARGIN_MM))
+    ax.set_ylim(ymin, ymax)
+    ax.set_title(f"Distance: {dist} (bad lighting)")
+    ax.set_xlabel("Frame")
+    ax.set_ylabel("z (mm)")
+    ax.legend(fontsize=7)
+    ax.grid(True, alpha=0.3)
+
+
+def main() -> None:
+    """Read CSV and produce combined + individual PNGs per distance."""
+    df = pd.read_csv(LOG_FILE)
+    OUT_DIR.mkdir(exist_ok=True)
+
+    distances = sorted(df["test_distance"].unique())
+    n = len(distances)
+
+    fig, axes = plt.subplots(1, n, figsize=(5 * n, 4), sharey=False)
+    if n == 1:
+        axes = [axes]
+    fig.suptitle("OAK-D SpatialDetectionNetwork — z accuracy over time (bad lighting)", fontsize=13)
+
+    for ax, dist, color in zip(axes, distances, COLORS):
+        subset = df[df["test_distance"] == dist].copy().sort_values("timestamp")
+        raw_z = subset["z_mm"].reset_index(drop=True)
+        nominal = NOMINAL_MM.get(dist)
+        plot_distance(ax, dist, raw_z, nominal, color)
+
+    fig.tight_layout()
+    out_path = OUT_DIR / "z_accuracy_all_bad_lighting.png"
+    fig.savefig(out_path, dpi=150)
+    plt.close(fig)
+    print(f"Saved {out_path}")
+
+    for dist, color in zip(distances, COLORS):
+        subset = df[df["test_distance"] == dist].copy().sort_values("timestamp")
+        raw_z = subset["z_mm"].reset_index(drop=True)
+        nominal = NOMINAL_MM.get(dist)
+
+        fig2, ax2 = plt.subplots(figsize=(8, 4))
+        plot_distance(ax2, dist, raw_z, nominal, color)
+        fig2.tight_layout()
+        fname = OUT_DIR / f"z_{dist.replace('.', '_')}_bad_lighting.png"
+        fig2.savefig(fname, dpi=150)
+        plt.close(fig2)
+        print(f"Saved {fname}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/graph1_z_over_time.png b/graph1_z_over_time.png
new file mode 100644
index 0000000..c6f1d07
Binary files /dev/null and b/graph1_z_over_time.png differ
diff --git a/graphs/z_0_5m_bad_lighting.png b/graphs/z_0_5m_bad_lighting.png
new file mode 100644
index 0000000..804b195
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diff --git a/graphs/z_1_0m_bad_lighting.png b/graphs/z_1_0m_bad_lighting.png
new file mode 100644
index 0000000..3b382ce
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diff --git a/graphs/z_1_5m_bad_lighting.png b/graphs/z_1_5m_bad_lighting.png
new file mode 100644
index 0000000..0c0aa77
Binary files /dev/null and b/graphs/z_1_5m_bad_lighting.png differ
diff --git a/graphs/z_2_0m_bad_lighting.png b/graphs/z_2_0m_bad_lighting.png
new file mode 100644
index 0000000..5762e37
Binary files /dev/null and b/graphs/z_2_0m_bad_lighting.png differ
diff --git a/graphs/z_3_0m_bad_lighting.png b/graphs/z_3_0m_bad_lighting.png
new file mode 100644
index 0000000..342a651
Binary files /dev/null and b/graphs/z_3_0m_bad_lighting.png differ
diff --git a/graphs/z_accuracy_all_bad_lighting.png b/graphs/z_accuracy_all_bad_lighting.png
new file mode 100644
index 0000000..f535d61
Binary files /dev/null and b/graphs/z_accuracy_all_bad_lighting.png differ
diff --git a/main_2025.py b/main_2025.py
index 1b7c348..e26aee3 100644
--- a/main_2025.py
+++ b/main_2025.py
@@ -1,15 +1,106 @@
 """
-for target tracking
+Target tracking pipeline for OAK-D.
+Runs YOLOv4-tiny spatial detection + object tracking on-device.
 """
 
 import pathlib
 
+import cv2
+import depthai as dai
+import yaml
+
+from modules.target_tracking.stereo_node import create_stereo_depth
+from modules.target_tracking.spatial_detection_node import create_spatial_detection_network
+from modules.target_tracking.object_tracker_node import create_object_tracker
 
 CONFIG_FILE_PATH = pathlib.Path("config.yaml")
+OUTPUT_QUEUE_SIZE = 4
+
+# Z-bias calibration anchors: (raw_z_mm, offset_mm_to_subtract).
+# Measured at 0.5/1.0/1.5/2.0/2.5m; final anchor tapers smoothly to factory calibration.
+# Beyond the last anchor the raw camera value is trusted as-is.
+Z_CALIBRATION_ANCHORS = (
+    (527.5, 27.5),  # 0.5m
+    (1075.1, 75.1),  # 1.0m
+    (1573.2, 73.2),  # 1.5m
+    (1893.1, -106.9),  # 2.0m
+    (2599.2, 99.2),  # 2.5m
+    (2800.0, 0.0),  # taper end — trust factory beyond this
+)
+
+
+def calibrate_z(raw_z: float) -> float:
+    """Apply piecewise-linear bias correction to a raw stereo-depth z value (mm)."""
+    if raw_z <= Z_CALIBRATION_ANCHORS[0][0]:
+        return raw_z - Z_CALIBRATION_ANCHORS[0][1]
+    if raw_z >= Z_CALIBRATION_ANCHORS[-1][0]:
+        return raw_z
+    for (z0, o0), (z1, o1) in zip(Z_CALIBRATION_ANCHORS, Z_CALIBRATION_ANCHORS[1:]):
+        if z0 <= raw_z <= z1:
+            t = (raw_z - z0) / (z1 - z0)
+            return raw_z - (o0 + t * (o1 - o0))
+    return raw_z
 
 
 def main() -> int:
-    """Main Function for target tracking"""
+    """Run the OAK-D target tracking pipeline."""
+    with open(CONFIG_FILE_PATH, "r", encoding="utf-8") as config_file:
+        config = yaml.safe_load(config_file)
+
+    model_name: str = config["spatial_detection"]["model_name"]
+
+    with dai.Pipeline() as pipeline:
+        stereo = create_stereo_depth(pipeline)
+        spatial_detection = create_spatial_detection_network(pipeline, stereo, model_name)
+        tracker = create_object_tracker(pipeline, spatial_detection)
+
+        tracklet_queue = tracker.out.createOutputQueue(maxSize=OUTPUT_QUEUE_SIZE, blocking=False)
+        preview_queue = tracker.passthroughTrackerFrame.createOutputQueue(
+            maxSize=OUTPUT_QUEUE_SIZE, blocking=False
+        )
+
+        pipeline.start()
+        while pipeline.isRunning():
+            tracklets_msg = tracklet_queue.get()
+            frame_msg = preview_queue.get()
+            frame = frame_msg.getCvFrame()
+
+            for tracklet in tracklets_msg.tracklets:
+                if tracklet.status != dai.Tracklet.TrackingStatus.TRACKED:
+                    continue
+
+                roi = tracklet.roi.denormalize(frame.shape[1], frame.shape[0])
+                x_mm = tracklet.spatialCoordinates.x
+                y_mm = tracklet.spatialCoordinates.y
+                z_mm = calibrate_z(tracklet.spatialCoordinates.z)
+
+                print(
+                    f"Target ID {tracklet.id}: "
+                    f"xyz=({x_mm:.0f}mm, {y_mm:.0f}mm, {z_mm:.0f}mm)  "
+                    f"bbox=({int(roi.topLeft().x)}, {int(roi.topLeft().y)}, "
+                    f"{int(roi.bottomRight().x)}, {int(roi.bottomRight().y)})"
+                )
+
+                cv2.rectangle(
+                    frame,
+                    (int(roi.topLeft().x), int(roi.topLeft().y)),
+                    (int(roi.bottomRight().x), int(roi.bottomRight().y)),
+                    (0, 255, 0),
+                    2,
+                )
+                cv2.putText(
+                    frame,
+                    f"ID {tracklet.id} | {z_mm:.0f}mm",
+                    (int(roi.topLeft().x), int(roi.topLeft().y) - 8),
+                    cv2.FONT_HERSHEY_SIMPLEX,
+                    0.5,
+                    (0, 255, 0),
+                    1,
+                )
+
+            cv2.imshow("Target Tracking", frame)
+            if cv2.waitKey(1) == ord("q"):
+                break
 
     return 0
 
@@ -18,5 +109,4 @@ def main() -> int:
     result_main = main()
     if result_main < 0:
         print(f"ERROR: Status code: {result_main}")
-
     print("Done!")
diff --git a/modules/target_tracking/__init__.py b/modules/target_tracking/__init__.py
index e69de29..603a418 100644
--- a/modules/target_tracking/__init__.py
+++ b/modules/target_tracking/__init__.py
@@ -0,0 +1 @@
+from .depthai_detector import DepthAIDetector
\ No newline at end of file
diff --git a/modules/target_tracking/depthai_detector.py b/modules/target_tracking/depthai_detector.py
new file mode 100644
index 0000000..1c8cd11
--- /dev/null
+++ b/modules/target_tracking/depthai_detector.py
@@ -0,0 +1,99 @@
+"""
+DepthAI detector implementation using Pipeline 2.0 API.
+"""
+import depthai as dai
+import numpy as np
+import cv2
+from .stereo_node import create_stereo_depth
+
+class DepthAIDetector:
+    """
+    DepthAI detector class using SpatialDetectionNetwork.
+    """
+    def __init__(self, pipeline: "dai.Pipeline"):
+        self.pipeline = pipeline
+        self.latest_detections = []
+        self.latest_frames = {}
+
+    class DataCollector(dai.node.HostNode):
+        """
+        Host node to collect data from the pipeline and make it available to the detector class.
+        """
+        def __init__(self, detector):
+            dai.node.HostNode.__init__(self)
+            self.detector = detector
+            self.sendProcessingToPipeline(True)
+
+        def build(self, depth: dai.Node.Output, detections: dai.Node.Output, rgb: dai.Node.Output):
+            self.link_args(depth, detections, rgb)
+
+        def process(self, depthPreview, detections, rgbPreview):
+            # Store data in the parent detector instance
+            self.detector.latest_frames["depth"] = depthPreview.getCvFrame()
+            self.detector.latest_frames["rgb"] = rgbPreview.getCvFrame()
+            self.detector.latest_detections = detections.detections
+
+    @classmethod
+    def create(cls, config: dict) -> "tuple[bool, DepthAIDetector | None]":
+        try:
+            model_name = config.get("model_path", "yolov6-nano")
+            size = config.get("input_size", (640, 400))
+            depth_source_type = config.get("depth_source", "stereo")
+            fps = config.get("fps", 30)
+
+            p = dai.Pipeline()
+            platform = p.getDefaultDevice().getPlatform()
+
+            # Define sources
+            cam_rgb = p.create(dai.node.Camera).build(dai.CameraBoardSocket.CAM_A, sensorFps=fps)
+
+            # Define depth source
+            if depth_source_type == "stereo":
+                depth_source = create_stereo_depth(p)
+                if platform == dai.Platform.RVC2:
+                    depth_source.setOutputSize(*size)
+            else:
+                print(f"Invalid depth source: {depth_source_type}")
+                return False, None
+
+            # Spatial Detection Network
+            model_description = dai.NNModelDescription(model_name)
+            spatial_nn = p.create(dai.node.SpatialDetectionNetwork).build(
+                cam_rgb, depth_source, model_description
+            )
+            
+            # Settings
+            spatial_nn.input.setBlocking(False)
+            spatial_nn.setBoundingBoxScaleFactor(0.5)
+            spatial_nn.setDepthLowerThreshold(100)
+            spatial_nn.setDepthUpperThreshold(5000)
+
+            # Create detector instance
+            detector = cls(p)
+            
+            # Use HostNode to collect data
+            collector = p.create(cls.DataCollector, detector)
+            collector.build(
+                spatial_nn.passthroughDepth,
+                spatial_nn.out,
+                spatial_nn.passthrough,
+            )
+
+            # Start the pipeline in the background
+            p.start()
+            
+            return True, detector
+        except Exception as e:
+            print(f"Error creating DepthAIDetector: {e}")
+            return False, None
+
+    def run(self) -> "tuple[bool, list, dict]":
+        """
+        Returns the latest detections and frames collected by the HostNode.
+        """
+        # Since the pipeline is running in the background, we just return the latest captured data.
+        return True, self.latest_detections, self.latest_frames
+
+    def __del__(self):
+        if hasattr(self, 'pipeline'):
+            self.pipeline.stop()
\ No newline at end of file
diff --git a/modules/target_tracking/object_tracker_node.py b/modules/target_tracking/object_tracker_node.py
new file mode 100644
index 0000000..2bd3859
--- /dev/null
+++ b/modules/target_tracking/object_tracker_node.py
@@ -0,0 +1,46 @@
+"""
+Module for initializing the Object Tracker node.
+Wraps DepthAI's ObjectTracker to track detections across frames with persistent IDs.
+"""
+
+import depthai as dai
+
+
+# Track only class 0 (person in COCO dataset)
+PERSON_CLASS_ID = 0
+
+# ZERO_TERM_COLOR_HISTOGRAM: lightweight, works without re-identification network
+TRACKER_TYPE = dai.TrackerType.ZERO_TERM_COLOR_HISTOGRAM
+
+# SMALLEST_ID: reuse lowest available ID, keeps IDs stable across frames
+ASSIGNMENT_POLICY = dai.TrackerIdAssignmentPolicy.SMALLEST_ID
+
+
+def create_object_tracker(
+    pipeline: dai.Pipeline,
+    spatial_detection: dai.node.SpatialDetectionNetwork,
+) -> dai.node.ObjectTracker:
+    """
+    Creates the ObjectTracker node and links it to the spatial detection network.
+
+    Args:
+        pipeline: The DepthAI pipeline object.
+        spatial_detection: The configured SpatialDetectionNetwork node.
+
+    Returns:
+        Configured ObjectTracker node.
+    """
+    tracker = pipeline.create(dai.node.ObjectTracker)
+
+    # Only track humans (COCO class 0)
+    tracker.setDetectionLabelsToTrack([PERSON_CLASS_ID])
+    tracker.setTrackerType(TRACKER_TYPE)
+    tracker.setTrackerIdAssignmentPolicy(ASSIGNMENT_POLICY)
+    tracker.setRunOnHost(True)
+
+    # passthrough provides the preview frame used to extract appearance features
+    spatial_detection.passthrough.link(tracker.inputTrackerFrame)
+    spatial_detection.passthrough.link(tracker.inputDetectionFrame)
+    spatial_detection.out.link(tracker.inputDetections)
+
+    return tracker
diff --git a/modules/target_tracking/spatial_detection_node.py b/modules/target_tracking/spatial_detection_node.py
new file mode 100644
index 0000000..b6d062a
--- /dev/null
+++ b/modules/target_tracking/spatial_detection_node.py
@@ -0,0 +1,48 @@
+"""
+Module for initializing the Spatial Detection Network.
+Integrates the stereo depth node with YOLO for 3D object localization.
+"""
+
+import depthai as dai
+
+
+# Detection thresholds
+CONFIDENCE_THRESHOLD = 0.5
+
+# Depth ROI scale (fraction of bounding box used to sample depth)
+BOUNDING_BOX_SCALE_FACTOR = 0.5
+
+# Valid depth range in mm
+DEPTH_LOWER_THRESHOLD_MM = 100
+DEPTH_UPPER_THRESHOLD_MM = 10000
+
+
+def create_spatial_detection_network(
+    pipeline: dai.Pipeline,
+    stereo: dai.node.StereoDepth,
+    model_name: str,
+) -> dai.node.SpatialDetectionNetwork:
+    """
+    Creates the SpatialDetectionNetwork and links it with stereo depth + color camera.
+
+    Args:
+        pipeline: The DepthAI pipeline object.
+        stereo: Configured StereoDepth node with depth aligned to CAM_A.
+        model_name: Luxonis model zoo name (e.g. "yolov6-nano").
+
+    Returns:
+        Configured SpatialDetectionNetwork node.
+    """
+    # CAM_A is the RGB sensor — must match stereo's setDepthAlign(CAM_A)
+    cam = pipeline.create(dai.node.Camera).build(dai.CameraBoardSocket.CAM_A)
+
+    spatial_detection = pipeline.create(dai.node.SpatialDetectionNetwork).build(
+        cam, stereo, model_name
+    )
+    spatial_detection.setConfidenceThreshold(CONFIDENCE_THRESHOLD)
+    spatial_detection.setBoundingBoxScaleFactor(BOUNDING_BOX_SCALE_FACTOR)
+    spatial_detection.setDepthLowerThreshold(DEPTH_LOWER_THRESHOLD_MM)
+    spatial_detection.setDepthUpperThreshold(DEPTH_UPPER_THRESHOLD_MM)
+    spatial_detection.input.setBlocking(False)
+
+    return spatial_detection
diff --git a/modules/target_tracking/stereo_node.py b/modules/target_tracking/stereo_node.py
index 20b1660..f0dfa66 100644
--- a/modules/target_tracking/stereo_node.py
+++ b/modules/target_tracking/stereo_node.py
@@ -1,6 +1,6 @@
 """
 Module for initializing and configuring the StereoDepth node.
-This setup aligns the depth map to the RGB camera for spatial logic.
+Depth aligned to CAM_A (RGB) for spatial detection.
 """
 
 import depthai as dai
@@ -8,42 +8,24 @@
 
 def create_stereo_depth(pipeline: dai.Pipeline) -> dai.node.StereoDepth:
     """
-    Creates the StereoDepth node and links it to the Mono cameras.
+    Creates the StereoDepth node and links it to the mono cameras.
 
     Args:
-        pipeline (dai.Pipeline): The DepthAI pipeline object.
+        pipeline: The DepthAI pipeline object.
 
     Returns:
-        dai.node.StereoDepth: The configured stereo node.
+        Configured StereoDepth node with depth aligned to CAM_A.
     """
-    # --- 1. Define Sources  ---
-    mono_left = pipeline.create(dai.node.MonoCamera)
-    mono_right = pipeline.create(dai.node.MonoCamera)
+    mono_left = pipeline.create(dai.node.Camera).build(dai.CameraBoardSocket.CAM_B)
+    mono_right = pipeline.create(dai.node.Camera).build(dai.CameraBoardSocket.CAM_C)
 
-    # Configure the hardware sockets (Left vs Right)
-    mono_left.setBoardSocket(dai.CameraBoardSocket.LEFT)
-    mono_right.setBoardSocket(dai.CameraBoardSocket.RIGHT)
-
-    # Set Resolution (400p is standard)
-    # Breaking line to satisfy flake8 line length limit
-    mono_left.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
-    mono_right.setResolution(dai.MonoCameraProperties.SensorResolution.THE_400_P)
-
-    # --- 2. Define the Processor  ---
     stereo = pipeline.create(dai.node.StereoDepth)
-
-    # --- 3. Configuration ---
-    # CRITICAL: Align depth to RGB (bc mono cams are 20 pixels off)
-    stereo.setDepthAlign(dai.CameraBoardSocket.RGB)
-
-    # Improve quality
+    stereo.setDepthAlign(dai.CameraBoardSocket.CAM_A)
     stereo.setSubpixel(True)
-    stereo.setLeftRightCheck(True)  # Removes ghost pixels at edges
-    # Change to True if <50cm need tracking needed
+    stereo.setLeftRightCheck(True)
     stereo.setExtendedDisparity(False)
 
-    # --- 4. Linking ---
-    mono_left.out.link(stereo.left)
-    mono_right.out.link(stereo.right)
+    mono_left.requestOutput((640, 400)).link(stereo.left)
+    mono_right.requestOutput((640, 400)).link(stereo.right)
 
     return stereo
diff --git a/record.py b/record.py
new file mode 100644
index 0000000..5a3343f
--- /dev/null
+++ b/record.py
@@ -0,0 +1,163 @@
+"""
+Accuracy recording script for OAK-D depth calibration.
+
+Controls:
+  Press 1 → 5s countdown then record 10s at 0.5m
+  Press 2 → 5s countdown then record 10s at 1.0m
+  Press 3 → 5s countdown then record 10s at 1.5m
+  Press 4 → 5s countdown then record 10s at 2.0m
+  Press 5 → 5s countdown then record 10s at 3.0m
+  Press s → stop recording early
+  Press q → quit
+"""
+
+import csv
+import pathlib
+import time
+
+import cv2
+import depthai as dai
+import yaml
+
+from modules.target_tracking.stereo_node import create_stereo_depth
+from modules.target_tracking.spatial_detection_node import create_spatial_detection_network
+from modules.target_tracking.object_tracker_node import create_object_tracker
+
+REPO_DIR = pathlib.Path(__file__).parent
+CONFIG_FILE_PATH = REPO_DIR / "config.yaml"
+LOG_FILE = REPO_DIR / "accuracy_log.csv"
+OUTPUT_QUEUE_SIZE = 4
+COUNTDOWN_SECS = 5
+RECORD_SECS = 10
+
+DISTANCE_KEYS = {
+    ord("1"): "0.5m",
+    ord("2"): "1.0m",
+    ord("3"): "1.5m",
+    ord("4"): "2.0m",
+    ord("5"): "3.0m",
+}
+
+
+def main() -> None:
+    """Run the recording pipeline."""
+    with open(CONFIG_FILE_PATH, "r", encoding="utf-8") as f:
+        config = yaml.safe_load(f)
+    model_name: str = config["spatial_detection"]["model_name"]
+
+    write_header = not LOG_FILE.exists() or LOG_FILE.stat().st_size == 0
+    log_file = open(LOG_FILE, "a", newline="", encoding="utf-8")  # noqa: SIM115
+    writer = csv.writer(log_file)
+    if write_header:
+        writer.writerow(["timestamp", "test_distance", "target_id", "x_mm", "y_mm", "z_mm"])
+
+    active_distance: str = ""
+    countdown_until: float = 0.0
+    countdown_target: str = ""
+    logging_until: float = 0.0
+
+    print("Controls: [1]=0.5m  [2]=1.0m  [3]=1.5m  [4]=2.0m  [5]=3.0m  [s]=stop  [q]=quit")
+
+    with dai.Pipeline() as pipeline:
+        stereo = create_stereo_depth(pipeline)
+        spatial_detection = create_spatial_detection_network(pipeline, stereo, model_name)
+        tracker = create_object_tracker(pipeline, spatial_detection)
+
+        tracklet_queue = tracker.out.createOutputQueue(maxSize=OUTPUT_QUEUE_SIZE, blocking=False)
+        preview_queue = tracker.passthroughTrackerFrame.createOutputQueue(
+            maxSize=OUTPUT_QUEUE_SIZE, blocking=False
+        )
+
+        pipeline.start()
+        while pipeline.isRunning():
+            tracklets_msg = tracklet_queue.get()
+            frame_msg = preview_queue.get()
+            frame = frame_msg.getCvFrame()
+            now = time.time()
+
+            if countdown_until and now >= countdown_until:
+                active_distance = countdown_target
+                logging_until = now + RECORD_SECS
+                countdown_until = 0.0
+                countdown_target = ""
+                print(f"\n--- Recording {active_distance} for {RECORD_SECS}s ---")
+
+            if active_distance and now >= logging_until:
+                print(f"\n--- Done recording {active_distance} ---")
+                active_distance = ""
+                logging_until = 0.0
+
+            for tracklet in tracklets_msg.tracklets:
+                if tracklet.status != dai.Tracklet.TrackingStatus.TRACKED:
+                    continue
+
+                roi = tracklet.roi.denormalize(frame.shape[1], frame.shape[0])
+                x_mm = tracklet.spatialCoordinates.x
+                y_mm = tracklet.spatialCoordinates.y
+                z_mm = tracklet.spatialCoordinates.z
+
+                if active_distance:
+                    writer.writerow(
+                        [
+                            round(now, 3),
+                            active_distance,
+                            tracklet.id,
+                            round(x_mm),
+                            round(y_mm),
+                            round(z_mm),
+                        ]
+                    )
+                    log_file.flush()
+
+                cv2.rectangle(
+                    frame,
+                    (int(roi.topLeft().x), int(roi.topLeft().y)),
+                    (int(roi.bottomRight().x), int(roi.bottomRight().y)),
+                    (0, 255, 0),
+                    2,
+                )
+                cv2.putText(
+                    frame,
+                    f"ID {tracklet.id} | {z_mm:.0f}mm",
+                    (int(roi.topLeft().x), int(roi.topLeft().y) - 8),
+                    cv2.FONT_HERSHEY_SIMPLEX,
+                    0.5,
+                    (0, 255, 0),
+                    1,
+                )
+
+            if countdown_until:
+                secs_left = max(0, int(countdown_until - now) + 1)
+                label = f"Get ready ({countdown_target})... {secs_left}s"
+                color = (0, 140, 255)
+            elif active_distance:
+                secs_left = max(0, int(logging_until - now) + 1)
+                label = f"Recording {active_distance}... {secs_left}s"
+                color = (0, 200, 100)
+            else:
+                label = "Idle  [1]=0.5m [2]=1.0m [3]=1.5m [4]=2.0m [5]=3.0m"
+                color = (160, 160, 160)
+            cv2.putText(frame, label, (10, 24), cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2)
+
+            cv2.imshow("Record Accuracy", frame)
+            key = cv2.waitKey(1) & 0xFF
+
+            if key == ord("q"):
+                break
+            elif key in DISTANCE_KEYS:
+                countdown_until = time.time() + COUNTDOWN_SECS
+                countdown_target = DISTANCE_KEYS[key]
+                active_distance = ""
+                print(f"\n--- {COUNTDOWN_SECS}s countdown — stand at {DISTANCE_KEYS[key]} ---")
+            elif key == ord("s"):
+                print(f"\n--- Stopped ({active_distance or countdown_target}) ---")
+                active_distance = ""
+                countdown_until = 0.0
+                countdown_target = ""
+
+    log_file.close()
+    print(f"Saved to {LOG_FILE}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/requirements-pytorch.txt b/requirements-pytorch.txt
index b805223..092d3e1 100644
--- a/requirements-pytorch.txt
+++ b/requirements-pytorch.txt
@@ -1,4 +1,6 @@
 # Runtime dependencies for target tracking pipeline
 
-depthai>=2.24.0
-pymavlink>=2.4.40
\ No newline at end of file
+depthai>=3.5.0
+opencv-python>=4.9.0
+pymavlink>=2.4.40
+pyyaml>=6.0
diff --git a/requirements.txt b/requirements.txt
index 2309fad..0aa13df 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,5 @@
-
 black==24.2.0
 flake8-annotations==3.0.1
 pylint==3.0.3
+ultralytics
+depthai
\ No newline at end of file
diff --git a/tests/unit/test_pipeline_nodes.py b/tests/unit/test_pipeline_nodes.py
new file mode 100644
index 0000000..88a17cc
--- /dev/null
+++ b/tests/unit/test_pipeline_nodes.py
@@ -0,0 +1,206 @@
+"""
+Unit tests for the target tracking pipeline nodes.
+Mocks depthai and blobconverter so no OAK-D hardware is required.
+"""
+
+import sys
+from unittest.mock import MagicMock, call, patch
+
+import pytest
+
+
+# ---------------------------------------------------------------------------
+# Mock depthai + blobconverter BEFORE importing any project modules that
+# import them at module level.  This must happen at collection time.
+# ---------------------------------------------------------------------------
+_dai = MagicMock()
+
+# Give TrackerType / TrackerIdAssignmentPolicy real-ish sentinel values so
+# the modules can assign them to constants without blowing up.
+_dai.TrackerType.ZERO_TERM_COLOR_HISTOGRAM = "ZERO_TERM_COLOR_HISTOGRAM"
+_dai.TrackerIdAssignmentPolicy.SMALLEST_ID = "SMALLEST_ID"
+_dai.MonoCameraProperties.SensorResolution.THE_400_P = "400P"
+_dai.ColorCameraProperties.ColorOrder.BGR = "BGR"
+_dai.CameraBoardSocket.LEFT = "LEFT"
+_dai.CameraBoardSocket.RIGHT = "RIGHT"
+_dai.CameraBoardSocket.RGB = "RGB"
+
+sys.modules["depthai"] = _dai
+sys.modules["blobconverter"] = MagicMock()
+
+# Now safe to import project modules
+from modules.target_tracking.stereo_node import create_stereo_depth  # noqa: E402
+from modules.target_tracking.spatial_detection_node import (  # noqa: E402
+    create_spatial_detection_network,
+)
+from modules.target_tracking.object_tracker_node import create_object_tracker  # noqa: E402
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+
+def _make_pipeline():
+    """Return a fresh mock pipeline whose create() returns distinct mocks."""
+    pipeline = MagicMock()
+    pipeline.create.side_effect = lambda node_type: MagicMock(name=str(node_type))
+    return pipeline
+
+
+# ---------------------------------------------------------------------------
+# stereo_node tests
+# ---------------------------------------------------------------------------
+
+
+class TestCreateStereoDepth:
+    def test_returns_stereo_node(self):
+        pipeline = _make_pipeline()
+        stereo = create_stereo_depth(pipeline)
+        assert stereo is not None
+
+    def test_creates_three_nodes(self):
+        """Expects: mono_left, mono_right, stereo = 3 pipeline.create() calls."""
+        pipeline = _make_pipeline()
+        create_stereo_depth(pipeline)
+        assert pipeline.create.call_count == 3
+
+    def test_depth_aligned_to_rgb(self):
+        pipeline = _make_pipeline()
+        stereo = create_stereo_depth(pipeline)
+        stereo.setDepthAlign.assert_called_once_with(_dai.CameraBoardSocket.RGB)
+
+    def test_quality_flags_set(self):
+        pipeline = _make_pipeline()
+        stereo = create_stereo_depth(pipeline)
+        stereo.setSubpixel.assert_called_once_with(True)
+        stereo.setLeftRightCheck.assert_called_once_with(True)
+        stereo.setExtendedDisparity.assert_called_once_with(False)
+
+    def test_mono_cameras_linked_to_stereo(self):
+        """mono_left.out.link and mono_right.out.link must each be called once."""
+        nodes = []
+        pipeline = MagicMock()
+        pipeline.create.side_effect = lambda _: (nodes.append(MagicMock()) or nodes[-1])
+
+        create_stereo_depth(pipeline)
+
+        # nodes[0]=mono_left, nodes[1]=mono_right, nodes[2]=stereo
+        mono_left, mono_right, _ = nodes
+        mono_left.out.link.assert_called_once()
+        mono_right.out.link.assert_called_once()
+
+
+# ---------------------------------------------------------------------------
+# spatial_detection_node tests
+# ---------------------------------------------------------------------------
+
+
+class TestCreateSpatialDetectionNetwork:
+    def test_returns_network_and_camera(self):
+        pipeline = _make_pipeline()
+        stereo = MagicMock()
+        network, cam = create_spatial_detection_network(pipeline, stereo)
+        assert network is not None
+        assert cam is not None
+
+    def test_creates_two_nodes(self):
+        """Expects: color_cam + YoloSpatialDetectionNetwork = 2 calls."""
+        pipeline = _make_pipeline()
+        stereo = MagicMock()
+        create_spatial_detection_network(pipeline, stereo)
+        assert pipeline.create.call_count == 2
+
+    def test_color_camera_preview_size(self):
+        pipeline = _make_pipeline()
+        stereo = MagicMock()
+        _, cam = create_spatial_detection_network(pipeline, stereo)
+        cam.setPreviewSize.assert_called_once_with(416, 416)
+
+    def test_confidence_threshold_set(self):
+        pipeline = _make_pipeline()
+        stereo = MagicMock()
+        network, _ = create_spatial_detection_network(pipeline, stereo)
+        network.setConfidenceThreshold.assert_called_once_with(0.5)
+
+    def test_depth_linked_to_network(self):
+        """stereo.depth.link(spatial_detection.inputDepth) must be called."""
+        pipeline = _make_pipeline()
+        stereo = MagicMock()
+        network, _ = create_spatial_detection_network(pipeline, stereo)
+        stereo.depth.link.assert_called_once_with(network.inputDepth)
+
+    def test_color_preview_linked_to_network(self):
+        pipeline = _make_pipeline()
+        stereo = MagicMock()
+        network, cam = create_spatial_detection_network(pipeline, stereo)
+        cam.preview.link.assert_called_once_with(network.input)
+
+    def test_custom_model_path_used(self):
+        pipeline = _make_pipeline()
+        stereo = MagicMock()
+        network, _ = create_spatial_detection_network(
+            pipeline, stereo, model_path="/fake/model.blob"
+        )
+        network.setBlobPath.assert_called_once_with("/fake/model.blob")
+
+    def test_default_model_downloaded_when_no_path(self):
+        import blobconverter
+
+        blobconverter.from_zoo.reset_mock()
+        blobconverter.from_zoo.return_value = "/mocked/zoo_model.blob"
+        pipeline = _make_pipeline()
+        stereo = MagicMock()
+        network, _ = create_spatial_detection_network(pipeline, stereo, model_path=None)
+        blobconverter.from_zoo.assert_called_once()
+        network.setBlobPath.assert_called_once_with("/mocked/zoo_model.blob")
+
+
+# ---------------------------------------------------------------------------
+# object_tracker_node tests
+# ---------------------------------------------------------------------------
+
+
+class TestCreateObjectTracker:
+    def test_returns_tracker(self):
+        pipeline = _make_pipeline()
+        spatial_detection = MagicMock()
+        tracker = create_object_tracker(pipeline, spatial_detection)
+        assert tracker is not None
+
+    def test_creates_one_node(self):
+        pipeline = _make_pipeline()
+        spatial_detection = MagicMock()
+        create_object_tracker(pipeline, spatial_detection)
+        assert pipeline.create.call_count == 1
+
+    def test_tracks_only_person_class(self):
+        pipeline = _make_pipeline()
+        spatial_detection = MagicMock()
+        tracker = create_object_tracker(pipeline, spatial_detection)
+        tracker.setDetectionLabelsToTrack.assert_called_once_with([0])
+
+    def test_tracker_type_set(self):
+        pipeline = _make_pipeline()
+        spatial_detection = MagicMock()
+        tracker = create_object_tracker(pipeline, spatial_detection)
+        tracker.setTrackerType.assert_called_once_with(
+            _dai.TrackerType.ZERO_TERM_COLOR_HISTOGRAM
+        )
+
+    def test_detections_linked_to_tracker(self):
+        """spatial_detection.out.link(tracker.inputDetections) must be called."""
+        pipeline = _make_pipeline()
+        spatial_detection = MagicMock()
+        tracker = create_object_tracker(pipeline, spatial_detection)
+        spatial_detection.out.link.assert_called_once_with(tracker.inputDetections)
+
+    def test_passthrough_linked_twice(self):
+        """passthrough links to both inputTrackerFrame and inputDetectionFrame."""
+        pipeline = _make_pipeline()
+        spatial_detection = MagicMock()
+        tracker = create_object_tracker(pipeline, spatial_detection)
+        assert spatial_detection.passthrough.link.call_count == 2
+        link_targets = {c.args[0] for c in spatial_detection.passthrough.link.call_args_list}
+        assert tracker.inputTrackerFrame in link_targets
+        assert tracker.inputDetectionFrame in link_targets