Homography-Based-MOTDT

• Term project results for AAA534 <Computer Vision> in Korea University
• This work is based on MOTDT which is one of the state-of-the-art algorithm for real-time multiple object tracking
  • code: https://github.com/longcw/MOTDT
  • paper: https://arxiv.org/abs/1809.04427
• For more information, please refer to the report file in this repository

Overview

• STEP1: Estimate bounding box of frame t+1 from the current frame t through Kalman Filter
• STEP2: Detect object at time t+1 using R-FCN
• STEP3: Filter objects estimated in STEP1 and objects detected in STEP2 through Non-Maximum Suppression
• STEP4: Calculate homography matrix from frame t and t+1
• STEP5: Create candidates by linearly transforming the existing object at time t through homography matrix obtained in STEP4
• STEP6: Allocate bounding box candidates from STEP3 and STEP5 to each object based on IOU and ReIE features.

Tracking Examples

MOT17 Dataset

MOTDT (original)

• The original model cannot maintain the track ID of object 1 (turned to 101), which is covered by object 105

Homography Based MOTDT (proposed)

• Ours maintains the track ID of object 1 and 89 even though they are obscured by object 161 carrying a green bag.

VisDrone Dataset

MOTDT (original)

• The original model cannot maintain the track ID of object 427 (turned to 509) due to a sudden change in camera angle

Homography Based MOTDT (proposed)

• Ours maintains the track ID of object 515 even though there is a sudden change in camera angle at the end of the clip

Results

MOT17 Dataset

	Original	Proposed
idf1	0.503	0.522
Mostly Tracked	59	70
Mostly Lost	151	152
False Positive	919	3,057
Num_Misses	28,580	26,781
Num_Switches	200	198
Num_Fragment	706	574
MOTA	0.428	0.421
MOTP	0.152	0.164

VisDrone Dataset

	Original	Proposed
idf1	0.547	0.579
Mostly Tracked	75	97
Mostly Lost	94	97
False Positive	725	3,064
Num_Misses	22,704	19,818
Num_Switches	504	386
Num_Fragment	1,604	806
MOTA	0.524	0.538
MOTP	0.094	0.116

Implications

• There has been a clear trade-off between the original and proposed method
• False Positive increased a lot with additional bounding boxes generated by Homography, while Mostly Tracked measure which means the tracking success in the 80% of whole frames improved
• Additionally, number of misses and number of fragments decreased considerably because of supplementary bounding boxes
• Tracking time increased enormously, which is main downside of proposed method