Method

MOTSFusion [MOTSFusion]
https://github.com/tobiasfshr/MOTSFusion

Submitted on 5 Dec. 2019 21:05 by
Jonathon Luiten (RWTH Aachen University)

Running time:0.44 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
First we build tracklets by calculating a
segmentation mask for each detection and linking
these over time using optical flow. We then fuse
these tracklets into 3D object reconstuctions
using depth and ego motion estimates. These 3D
reconstructions are then used to estimate the 3D
motion of objects, which is used to merge
tracklets into long-term tracks, bridging
occlusion gaps of up to 20 frames. This also
allows us to fill in missing detections.
Parameters:
Detections = RRC
Segmentations = BB2SegNet
Latex Bibtex:
@article{luiten2019MOTSFusion,
title={Track to Reconstruct and Reconstruct to
Track},
author={Luiten, Jonathon and Fischer, Tobias and
Leibe, Bastian},
journal={IEEE Robotics and Automation Letters},
year={2020},
publisher={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 73.63 % 75.44 % 72.39 % 78.32 % 90.78 % 75.53 % 89.97 % 90.29 %

Benchmark TP FP FN
CAR 31418 5342 295

Benchmark MOTSA MOTSP MODSA IDSW sMOTSA
CAR 84.12 % 89.31 % 84.67 % 201 74.98 %

Benchmark MT rate PT rate ML rate FRAG
CAR 66.07 % 27.78 % 6.16 % 544

Benchmark # Dets # Tracks
CAR 31713 685

This table as LaTeX