Method

Robust Tricks for Multi-Pedestrian Tracking [Rt_Track]
[Anonymous Submission]

Submitted on 31 Aug. 2023 17:44 by
[Anonymous Submission]

Running time:0.1 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
we design a novel direction consistency method for smooth trajectory prediction(STP-DC) to increase the modeling of motion information and overcome the lack of robustness in previous methods in complex scenes. We propose a hyper-grain feature embedding network (HG-FEN) to enhance the modeling of appearance models, thus generating robust appearance descriptors.We also proposed other robustness techniques, including CF-ECM for storing robust appearance information and SK-AS for improving association accuracy.
Parameters:
weighta=0.95
weightb=0.05
feature th=0.28
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 87.14 % 82.72 % 87.67 % 86.36 %
PEDESTRIAN 60.63 % 74.70 % 61.13 % 92.58 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 90.26 % 98.54 % 94.22 % 34562 511 3728 4.59 % 40906 916
PEDESTRIAN 66.19 % 93.27 % 77.43 % 15436 1114 7885 10.01 % 19227 315

Benchmark MT PT ML IDS FRAG
CAR 69.08 % 25.69 % 5.23 % 183 486
PEDESTRIAN 31.27 % 41.58 % 27.15 % 115 764

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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