Method

AttentionTrack [AT]


Submitted on 9 Sep. 2022 15:36 by
Chuang Zhang (Tsinghua University)

Running time:0.03 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
On the one hand, we utilizes attention module to
aggregate features from different down-sampling
rate, which can improve the robustness of feature
encoding to complex traffic environment and
various object size. On the other hand, we use
attention module to reprocess the features encoded
by backbone network to generate specific features
of detection and tracking tasks.
Parameters:
alpha=0.2
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 76.59 % 76.27 % 77.47 % 79.59 % 86.30 % 80.95 % 87.70 % 86.97 %
PEDESTRIAN 50.73 % 50.78 % 50.94 % 54.80 % 72.28 % 58.02 % 65.85 % 78.39 %

Benchmark TP FP FN
CAR 31253 3139 468
PEDESTRIAN 16482 6668 1070

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 89.03 % 85.56 % 89.51 % 164 75.92 %
PEDESTRIAN 65.65 % 74.59 % 66.58 % 214 47.56 %

Benchmark MT rate PT rate ML rate FRAG
CAR 78.31 % 18.92 % 2.77 % 159
PEDESTRIAN 41.24 % 35.74 % 23.02 % 566

Benchmark # Dets # Tracks
CAR 31721 682
PEDESTRIAN 17552 281

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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