Method

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


Submitted on 18 Feb. 2020 12:30 by
Viktor Porokhonskyy (Samsung R&D Institute Ukraine)

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.

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
PEDESTRIAN 43.73 % 53.73 % 36.05 % 58.01 % 73.19 % 40.05 % 69.44 % 78.91 %

Benchmark TP FP FN
PEDESTRIAN 17308 5842 1042

Benchmark MOTA MOTP MODA IDSW sMOTA
PEDESTRIAN 67.31 % 74.88 % 70.26 % 683 48.53 %

Benchmark MT rate PT rate ML rate FRAG
PEDESTRIAN 46.05 % 45.02 % 8.93 % 913

Benchmark # Dets # Tracks
PEDESTRIAN 18350 786

This table as LaTeX