Method

SRK_ODESA(car) [on][at] [SRK_ODESA(mc)]


Submitted on 24 Dec. 2019 12:48 by
Viktor Porokhonskyy (Samsung R&D Institute Ukraine)

Running time:0.4 s
Environment:GPU (Python)

Method Description:
The corresponding solution employs tracking-by-detection approach. Its core is formed by an effective object embedding which enjoys several attractive properties. Namely, it is rather light-weight, offers expandability to the case of objects composed from multiple parts and demonstrates good generalization. The last property could be illustrated by the fact that no KITTI data was involved into the embedding training procedure.

The solution optimizes MOTA value.
Parameters:
private
Latex Bibtex:
@inproceedings{ODESA2020,
author = {Dmytro Mykheievskyi and
Dmytro Borysenko and
Viktor Porokhonskyy},
title = {Learning Local Feature Descriptors for Multiple Object Tracking},
booktitle = {ACCV},
year = {2020}
}

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 64.25 % 74.87 % 55.70 % 78.62 % 84.68 % 62.10 % 81.78 % 85.85 %

Benchmark TP FP FN
CAR 31431 2961 502

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 88.50 % 84.04 % 89.93 % 491 73.92 %

Benchmark MT rate PT rate ML rate FRAG
CAR 82.31 % 14.77 % 2.92 % 488

Benchmark # Dets # Tracks
CAR 31933 1043

This table as LaTeX

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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