Method

Multi-Object Tracking and Segmentation via Neural Message Passing [MPNTrackSeg]
TbD

Submitted on 30 Jun. 2022 16:15 by
Orcun Cetintas (Technical University of Munich)

Running time:0.08 s
Environment:8 cores @ 2.5 Ghz (Python)

Method Description:
Graph Neural Network-based edge-classification for
data association and segmentation.
Parameters:
All hyperparameters are specified in the code.
Latex Bibtex:
TbD

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 55.50 % 60.45 % 52.04 % 64.67 % 75.15 % 59.76 % 70.45 % 79.29 %

Benchmark TP FP FN
PEDESTRIAN 16954 3743 857

Benchmark MOTSA MOTSP MODSA IDSW sMOTSA
PEDESTRIAN 76.99 % 75.95 % 77.78 % 162 57.29 %

Benchmark MT rate PT rate ML rate FRAG
PEDESTRIAN 56.30 % 34.07 % 9.63 % 720

Benchmark # Dets # Tracks
PEDESTRIAN 17811 294

This table as LaTeX