Method

Improved YOLOv5 and NC2 Adaptive State Estimation for Laser Radar Multi-Target Tracking [la] [on] [FNC2-yolo]


Submitted on 29 Mar. 2023 17:42 by
li jian (China University of Science and Technology)

Running time:0.01 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
We propose a novel multi-target tracking algorithm for laser radar data, based on the improved YOLOv5 and NC2 adaptive state estimation. We introduce a new post-processing step, using a confidence score threshold to filter out false detection results. Our proposed tracker accurately estimates target positions and velocities and can handle complex scenes. The method has great potential for real-world applications such as surveillance, robotics, and autonomous driving.



Parameters:
python3.9
Latex Bibtex:

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 48.90 % 48.91 % 49.25 % 53.03 % 67.44 % 53.04 % 70.78 % 75.19 %

Benchmark TP FP FN
PEDESTRIAN 16563 6587 1642

Benchmark MOTA MOTP MODA IDSW sMOTA
PEDESTRIAN 63.28 % 70.17 % 64.45 % 271 41.94 %

Benchmark MT rate PT rate ML rate FRAG
PEDESTRIAN 45.36 % 32.65 % 21.99 % 942

Benchmark # Dets # Tracks
PEDESTRIAN 18205 471

This table as LaTeX

This figure as: png pdf

[1] J. Luiten, A. Os̆ep, P. Dendorfer, P. Torr, A. Geiger, L. Leal-Taixé, B. Leibe: HOTA: A Higher Order Metric for Evaluating Multi-object Tracking. IJCV 2020.
[2] K. Bernardin, R. Stiefelhagen: Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. JIVP 2008.
[3] Y. Li, C. Huang, R. Nevatia: Learning to associate: HybridBoosted multi-target tracker for crowded scene. CVPR 2009.


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