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Particle Swarm Optimization Based Support Vector Machine for Human Tracking

  • Zhenyuan XuEmail author
  • Chao Xu
  • Junzo Watada
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 56)

Abstract

Human tracking is one of the most important researches in computer vision. It is quite useful for many applications, such as surveillance systems and smart vehicle systems. It is also an important basic step for content analysis for behavior recognition and target detection. Due to the variations in human positions, complicated backgrounds and environmental conditions, human tracking remains challenging work. In particular, difficulties caused by environment and background such as occlusion and noises should be solved. Also, real-time human tracking now seems a critical step in intelligent video surveillance systems because of its huge computational workload. In this paper we propose a Particle Swarm Optimization based Support Vector Machine (PSO-SVM) to overcome these problems. First, we finish the preliminary human tracking step in several frames based on some filters such as particle filter and kalman filter. Second, for each newly come frame need to be processed, we use the proposed PSO-SVM to process the previous frames as a regression frame work, based on this regression frame work, an estimated location of the target will be calculated out. Third, we process the newly come frame based on the particle filter and calculate out the target location as the basic target location. Finally, based on comparison analysis between basic target location and estimated target location, we can get the tracked target location. Experiment results on several videos will show the effectiveness and robustness of the proposed method.

Keywords

Human tracking Occlusion Real-time Particle filter PSO-SVM 

Notes

Acknowledgments

This work was supported in part by Nanjing Audit University, Nanjing, Jiangsu, China and “six kinds of peak talents” high level support of Jiangsu.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Nanjing Audit UniversityPukou, NanjingChina
  2. 2.Graduate School of Information, Production and SystemsWaseda UniversityKitakyushu, FukuokaJapan

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