Method

Collaborated Multi Object Tracking-PermaTrack-DeepSORT[st] [CollabMOT-Permatrack]
[Anonymous Submission]

Submitted on 18 Jan. 2023 03:46 by
[Anonymous Submission]

Running time:0.05 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
Most of multi-object tracking methods based on
deep learning, however, are highly prone to
frequent tracking losses and track-ID switching in
case of limited viewpoint and occluded objects. To
alleviate this problem, we propose a multi-camera
Collaborate Multi Object Tracking (CollabMOT)
method which performs online association of
multiple tracked vehicles from stereo vision
camera. CollabMOT not only provides global
tracking IDs between multiple cameras but also
helps reduce the problem of ID switching, tracklet
missing and false positive compared with the
conventional multi-object tracking based on single
camera.
Parameters:
\sct_method=permatrack+deepsort
\maximum_overlapped=0.5
\frame_offset=0
Latex Bibtex:

Detailed Results

From all 29 test sequences, our benchmark computes the commonly used tracking metrics CLEARMOT, MT/PT/ML, identity switches, and fragmentations [1,2]. The tables below show all of these metrics.


Benchmark MOTA MOTP MODA MODP
CAR 92.09 % 85.78 % 92.49 % 88.46 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 94.30 % 99.01 % 96.60 % 36693 366 2218 3.29 % 46019 1621

Benchmark MT PT ML IDS FRAG
CAR 86.62 % 11.08 % 2.31 % 136 334

This table as LaTeX

[1] K. Bernardin, R. Stiefelhagen: Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. JIVP 2008.
[2] Y. Li, C. Huang, R. Nevatia: Learning to associate: HybridBoosted multi-target tracker for crowded scene. CVPR 2009.


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