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 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 75.19 % 75.55 % 75.36 % 78.77 % 86.04 % 78.34 % 88.24 % 86.59 %
PEDESTRIAN 42.73 % 44.71 % 41.15 % 50.24 % 67.27 % 46.55 % 64.60 % 78.30 %

Benchmark TP FP FN
CAR 31150 3242 334
PEDESTRIAN 15214 7936 2076

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 89.09 % 85.03 % 89.60 % 176 75.53 %
PEDESTRIAN 55.45 % 74.22 % 56.75 % 302 38.51 %

Benchmark MT rate PT rate ML rate FRAG
CAR 78.15 % 17.54 % 4.31 % 305
PEDESTRIAN 33.33 % 40.55 % 26.12 % 732

Benchmark # Dets # Tracks
CAR 31484 763
PEDESTRIAN 17290 427

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.


eXTReMe Tracker