Method

A Joint Multi-object Tracking Method with Center-based Feature Extraction and Occlusion Handling [CJMODT-v2]
[Anonymous Submission]

Submitted on 9 Nov. 2022 03:40 by
[Anonymous Submission]

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

Method Description:
This work introduces CJMODT, a center-based multi-
object
detection and tracking algorithm for MOT. It
includes a newly
proposed center-based feature extraction framework
for multi-
object detection.
Parameters:
λf= 0.5, λs= 0.1, and λo= 0.5
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 71.75 % 73.16 % 71.01 % 75.78 % 86.84 % 73.04 % 90.39 % 86.97 %
PEDESTRIAN 41.42 % 44.41 % 39.03 % 47.84 % 71.82 % 42.77 % 72.94 % 78.69 %

Benchmark TP FP FN
CAR 29772 4620 239
PEDESTRIAN 14217 8933 1202

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 84.86 % 85.56 % 85.87 % 347 72.36 %
PEDESTRIAN 54.09 % 74.81 % 56.22 % 493 38.62 %

Benchmark MT rate PT rate ML rate FRAG
CAR 66.15 % 27.54 % 6.31 % 317
PEDESTRIAN 31.61 % 40.55 % 27.84 % 681

Benchmark # Dets # Tracks
CAR 30011 963
PEDESTRIAN 15419 761

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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