Method

A Joint Multi-object Tracking Method with Center-based Feature Extraction and Occlusion Handling [CJMODT-v3]


Submitted on 11 Nov. 2022 17:53 by
Junnian Li (Beijing University of Chemical Technology)

Running time:0.05 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
This work proposes a joint MOT algorithm to handle
such occlusion. Pairs of frames in complicated
environments are taken as input. A center-based
feature extraction framework is designed for
precisely detecting objects and extracting their
feature maps. A ConvGRU module is applied to learn
permanent representations by using historical
spatio-temporal information of objects. A
Hungarian matching method is applied to match the
detected objects and predicted predictions.
Parameters:
λf = 1, λs= 0.1, and λo= 1
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 86.01 % 85.94 % 86.48 % 88.74 %
PEDESTRIAN 52.47 % 74.69 % 52.89 % 92.43 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 89.06 % 98.96 % 93.75 % 34870 368 4282 3.31 % 38643 1628
PEDESTRIAN 62.06 % 87.70 % 72.68 % 14506 2035 8870 18.29 % 19038 1381

Benchmark MT PT ML IDS FRAG
CAR 69.23 % 25.38 % 5.38 % 160 421
PEDESTRIAN 31.27 % 43.30 % 25.43 % 98 741

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.


eXTReMe Tracker