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 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
PEDESTRIAN 63.88 % 70.28 % 65.02 % 90.90 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
PEDESTRIAN 72.22 % 91.44 % 80.70 % 16935 1585 6513 14.25 % 20960 625

Benchmark MT PT ML IDS FRAG
PEDESTRIAN 46.39 % 31.62 % 21.99 % 264 1066

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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