Method

SRK_ODESA (a.k.a. SiRtaKi) [on][at] [SRK_ODESA]


Submitted on 18 Feb. 2020 12:30 by
()

Running time:0.5 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.
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 commonly used tracking metrics CLEARMOT, MT/PT/ML, identity switches, and fragmentations [1,2]. The tables below show all of these metrics.


Benchmark MOTA MOTP MODA MODP
PEDESTRIAN 69.88 % 75.07 % 70.70 % 91.85 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
PEDESTRIAN 75.15 % 94.65 % 83.78 % 17516 991 5791 8.91 % 22187 1108

Benchmark MT PT ML IDS FRAG
PEDESTRIAN 45.02 % 46.74 % 8.25 % 191 1070

This table as LaTeX


[1] K. Bernardin, R. Stiefelhagen: Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. JIVP 2008.
[2] Y. Li, C. Huang, R. Nevatia: Learning to associate: HybridBoosted multi-target tracker for crowded scene. CVPR 2009.


eXTReMe Tracker