Method

DEFT: Detection Embeddings for Tracking [on] [DEFT]
https://github.com/MedChaabane/DEFT

Submitted on 28 Oct. 2020 04:29 by
Mohamed Chaabane (Colorado State University)

Running time:0.04 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
DEFT is joint model of detection and tracking. Our approach relies on
an appearance-based object matching network jointly-learned with
an underlying object detection network. An LSTM is also added to
capture motion constraints.
Parameters:
TBD
Latex Bibtex:
@InProceedings{Chaabane2021deft_2021_CVPR_Workshops,
author = {Chaabane, Mohamed and Zhang, Peter and Beveridge,
Ross and O'Hara, Stephen},
title = {DEFT: Detection Embeddings for Tracking},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer
Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2021}
}

Detailed Results

From all 29 test sequences, our benchmark computes the commonly used tracking metrics CLEARMOT, MT/PT/ML, identity switches, and fragmentations [1,2]. The tables below show all of these metrics.


Benchmark MOTA MOTP MODA MODP
CAR 88.95 % 84.55 % 89.95 % 87.47 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 93.51 % 97.59 % 95.50 % 36697 908 2549 8.16 % 47021 1150

Benchmark MT PT ML IDS FRAG
CAR 84.77 % 13.38 % 1.85 % 343 553

This table as LaTeX

[1] K. Bernardin, R. Stiefelhagen: Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. JIVP 2008.
[2] Y. Li, C. Huang, R. Nevatia: Learning to associate: HybridBoosted multi-target tracker for crowded scene. CVPR 2009.


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