JOURNAL ARTICLE

Multitarget tracking using mean-shift with particle filter based initialization

Abstract

This paper presents a multitarget tracking algorithm based on mean-shift, in which an automatic initialization process by particle filter is utilized. In the standard mean-shift algorithm, this initialization is necessary to construct the reference target model to track. In our approach, switching between the particle filter based detection and the mean-shift tracking is introduced. Furthermore, we extend mean-shift tracking with particle filter based initialization into multitarget tracking problem. We experimentally show that our method can track multiple targets in outdoor situation and can run in real-time on a PC.

Keywords:
Initialization Mean-shift Particle filter Tracking (education) Computer science Filter (signal processing) Artificial intelligence Track (disk drive) Auxiliary particle filter Process (computing) Computer vision Algorithm Pattern recognition (psychology) Kalman filter Extended Kalman filter Ensemble Kalman filter

Metrics

5
Cited By
0.59
FWCI (Field Weighted Citation Impact)
11
Refs
0.75
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Video Surveillance and Tracking Methods
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Target Tracking and Data Fusion in Sensor Networks
Physical Sciences →  Computer Science →  Artificial Intelligence
Advanced Measurement and Detection Methods
Physical Sciences →  Engineering →  Electrical and Electronic Engineering

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Journal:   Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE Year: 2011 Vol: 8350 Pages: 83502J-83502J
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