Method

ViP-DeepLab [ViP-DeepLab]


Submitted on 9 Nov. 2020 23:55 by
Siyuan Qiao (Johns Hopkins University)

Running time:0.1 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
tbd
Parameters:
tbd
Latex Bibtex:
@article{vip_deeplab,
title={ViP-DeepLab: Learning Visual Perception
with Depth-aware Video Panoptic Segmentation},
author={Siyuan Qiao and Yukun Zhu and Hartwig
Adam and Alan Yuille and Liang-Chieh Chen},
journal={Proceedings of the IEEE Conference on
Computer Vision and Pattern Recognition},
year={2021},
}

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 76.38 % 82.70 % 70.93 % 88.70 % 88.77 % 75.86 % 86.00 % 90.75 %
PEDESTRIAN 64.31 % 70.69 % 59.48 % 75.71 % 81.77 % 67.52 % 74.92 % 84.40 %

Benchmark TP FP FN
CAR 35242 1518 1493
PEDESTRIAN 18432 2265 731

Benchmark MOTSA MOTSP MODSA IDSW sMOTSA
CAR 90.74 % 89.87 % 91.81 % 392 81.03 %
PEDESTRIAN 84.52 % 82.31 % 85.52 % 209 68.76 %

Benchmark MT rate PT rate ML rate FRAG
CAR 92.19 % 7.21 % 0.60 % 466
PEDESTRIAN 73.70 % 23.70 % 2.59 % 513

Benchmark # Dets # Tracks
CAR 36735 959
PEDESTRIAN 19163 406

This table as LaTeX