Method

Observation-Centric SORT [on] [OC-SORT]
https://github.com/noahcao/OC_SORT

Submitted on 25 Mar. 2022 14:12 by
Travis Song (University of California, San Diego)

Running time:0.03 s
Environment:1 core @ 3.0 Ghz (Python)

Method Description:
Use the detections from PermanceTrack. OC-SORT uses Kalman
Filter for tracking without using appearance. It makes improvement
over the standard SORT by recognizing its limitation in occlusion and
non-linear motion. The inference speed of tracking given off-shelf
detection is 700fps on a i9@3GHz CPU. The method keeps online,
simple and realtime.
Parameters:
detection confidence threshold = 0.6,
IoU matching threshold = 0.3
min_hits = 3
Latex Bibtex:
@misc{cao2022observationcentric,
title={Observation-Centric SORT: Rethinking SORT for Robust
Multi-Object Tracking},
author={Jinkun Cao and Xinshuo Weng and Rawal Khirodkar
and Jiangmiao Pang and Kris Kitani},
url={https://arxiv.org/abs/2203.14360},
year={2022},
eprint={2203.14360},
archivePrefix={arXiv},
primaryClass={cs.CV}
}

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 76.54 % 77.25 % 76.39 % 80.64 % 86.36 % 80.33 % 87.17 % 87.01 %
PEDESTRIAN 54.69 % 50.82 % 59.08 % 55.68 % 70.94 % 64.09 % 73.36 % 78.52 %

Benchmark TP FP FN
CAR 31707 2685 407
PEDESTRIAN 16728 6422 1443

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 90.28 % 85.53 % 91.01 % 250 76.95 %
PEDESTRIAN 65.14 % 74.53 % 66.03 % 204 46.74 %

Benchmark MT rate PT rate ML rate FRAG
CAR 80.00 % 16.92 % 3.08 % 280
PEDESTRIAN 44.33 % 35.74 % 19.93 % 609

Benchmark # Dets # Tracks
CAR 32114 705
PEDESTRIAN 18171 291

This table as LaTeX