Method

Multi-Camera Collaborative Multi Object Tracking-RAM-DeepSort [st] [CollabMOT-RAM]
[Anonymous Submission]

Submitted on 17 Jan. 2023 14:36 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. CMOT
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:
\maximum_overlapped=0.5
\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 91.88 % 85.86 % 92.60 % 88.58 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 94.85 % 98.58 % 96.68 % 37049 532 2012 4.78 % 45965 902

Benchmark MT PT ML IDS FRAG
CAR 86.92 % 10.62 % 2.46 % 248 372

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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