Method

Observation-Centric SORT [on] [OC-SORT]
[Anonymous Submission]

Submitted on 6 Mar. 2022 04:13 by
[Anonymous Submission]

Running time:0.01 s
Environment:8 cores @ 3.0 Ghz (Python)

Method Description:
An online method with pure motion model. No appearance features or
other cues are used. No heuristic post-processing is required. Given
detections, the inference speed is 793FPS on a Intel i9-9980XE CPU
@ 3.00GHz.
Parameters:
We directly uses the detection results from permaTrack with the
model trained on partial training data (available on its github repo:
https://github.com/TRI-ML/permatrack).
Latex Bibtex:

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 74.20 % 76.75 % 72.36 % 80.15 % 86.30 % 76.30 % 87.10 % 87.00 %
PEDESTRIAN 52.12 % 47.93 % 56.87 % 52.18 % 72.12 % 61.72 % 73.14 % 79.34 %

Benchmark TP FP FN
CAR 31476 2916 463
PEDESTRIAN 15460 7690 1288

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 89.31 % 85.50 % 90.17 % 299 76.04 %
PEDESTRIAN 60.16 % 75.78 % 61.22 % 245 43.98 %

Benchmark MT rate PT rate ML rate FRAG
CAR 81.54 % 15.54 % 2.92 % 303
PEDESTRIAN 36.77 % 39.52 % 23.71 % 561

Benchmark # Dets # Tracks
CAR 31939 750
PEDESTRIAN 16748 300

This table as LaTeX