Method

Fast MO Extrapolation Tracker [on] [extraCK]


Submitted on 10 Jan. 2018 12:40 by
Gultekin Gunduz (Galatasaray University)

Running time:0.03 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
Motion feature extrapolation and affinity cost
weighted by appearance features. Tracklet
assignment is done by solving linear sum
assignment problem.
Parameters:
Object Detection Threshold 0.4,
Latex Bibtex:
@inproceedings{gunduz2018lightweight,
title={A lightweight online multiple object
vehicle tracking method},
author={Gunduz, Gultekin and
Acarman, Tankut},
booktitle={Intelligent Vehicles Symposium
(IV), 2018 IEEE},
pages={427--432},
year={2018},
organization={IEEE}
}

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 59.76 % 65.18 % 55.47 % 69.21 % 81.69 % 61.82 % 75.70 % 84.30 %

Benchmark TP FP FN
CAR 28463 5929 675

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 79.29 % 82.06 % 80.80 % 520 64.44 %

Benchmark MT rate PT rate ML rate FRAG
CAR 62.31 % 31.85 % 5.85 % 750

Benchmark # Dets # Tracks
CAR 29138 871

This table as LaTeX