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 HOTA tracking metrics (HOTA, DetA, AssA, DetRe, DetPr, AssRe, AssPr, LocA) [1] as well as the CLEARMOT, MT/PT/ML, identity switches, and fragmentation [2,3] metrics. The tables below show all of these metrics.


Benchmark HOTA DetA AssA DetRe DetPr AssRe AssPr LocA
CAR 74.00 % 71.90 % 77.11 % 75.50 % 83.38 % 80.68 % 86.32 % 84.91 %
PEDESTRIAN 51.77 % 47.40 % 56.75 % 50.74 % 72.76 % 61.37 % 74.53 % 78.62 %

Benchmark TP FP FN
CAR 30604 3788 540
PEDESTRIAN 15254 7896 892

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 86.67 % 83.00 % 87.42 % 258 71.53 %
PEDESTRIAN 61.26 % 74.57 % 62.04 % 180 44.51 %

Benchmark MT rate PT rate ML rate FRAG
CAR 68.46 % 26.00 % 5.54 % 325
PEDESTRIAN 31.27 % 41.24 % 27.49 % 639

Benchmark # Dets # Tracks
CAR 31144 721
PEDESTRIAN 16146 266

This table as LaTeX

This figure as: png pdf

This figure as: png pdf

[1] J. Luiten, A. Os̆ep, P. Dendorfer, P. Torr, A. Geiger, L. Leal-Taixé, B. Leibe: HOTA: A Higher Order Metric for Evaluating Multi-object Tracking. IJCV 2020.
[2] K. Bernardin, R. Stiefelhagen: Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. JIVP 2008.
[3] Y. Li, C. Huang, R. Nevatia: Learning to associate: HybridBoosted multi-target tracker for crowded scene. CVPR 2009.


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