Method

Displacement Uncertainty for Occlusion Handling in Low-Frame-Rate Multiple Object Tracking [on] [APPTracker]


Submitted on 26 Jan. 2024 15:24 by
Tao Zhou (Zhejiang University)

Running time:0.04 s
Environment:GPU @ 1.5 Ghz (Python)

Method Description:
An online multi-object tracker designed for
occlusion handling in low-frame-rate scenarios. We
identify discontinuously visible targets by
detecting anomalies in optical flow estimation and
accordingly switch between vision cues and motion
cues.
Parameters:
See manuscript.
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 89.44 % 85.15 % 89.81 % 87.91 %
PEDESTRIAN 56.20 % 74.54 % 56.59 % 92.11 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 91.76 % 99.17 % 95.32 % 35706 299 3207 2.69 % 47845 1168
PEDESTRIAN 66.05 % 87.88 % 75.42 % 15413 2126 7923 19.11 % 24128 561

Benchmark MT PT ML IDS FRAG
CAR 78.62 % 17.54 % 3.85 % 125 415
PEDESTRIAN 32.30 % 42.27 % 25.43 % 90 854

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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