Method

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

Submitted on 28 Mar. 2019 13:43 by
Jonathon Luiten (RWTH Aachen University)

Running time:0.44s
Environment:GPU (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
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 68.74 % 72.19 % 66.16 % 76.05 % 84.88 % 69.57 % 85.49 % 86.56 %

Benchmark TP FP FN
CAR 30100 4292 713

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 84.24 % 85.03 % 85.45 % 415 71.14 %

Benchmark MT rate PT rate ML rate FRAG
CAR 72.77 % 24.31 % 2.92 % 569

Benchmark # Dets # Tracks
CAR 30813 929

This table as LaTeX