{"title":"多特征跟踪应用的改进粒子滤波","authors":"Zhelong Wang, Hongyu Zhao, Hong Shang, S. Qiu","doi":"10.1109/IST.2012.6295576","DOIUrl":null,"url":null,"abstract":"In order to improve the accuracy and robustness of real-time tracking system, this paper presents new methods for efficient object tracking in video sequences using multiple features and particle filter. Based on the problem that tracking with a single feature is susceptible to interference, the color and edge orientation features are combined under the particle filtering framework, and an adaptive feature-weight assignment approach is also proposed in the process of feature fusion. In the prediction period of particle filter algorithm, the mean-shift method is used to improve the particle swarm optimization algorithm. In this way, the number of effective particles is increased and the real-time performance of the tracking system is improved. Experiment results show that the proposed tracking system is more accurate and more efficient than the traditional color feature based mean-shift algorithm.","PeriodicalId":213330,"journal":{"name":"2012 IEEE International Conference on Imaging Systems and Techniques Proceedings","volume":"43 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"An improved particle filter for multi-feature tracking application\",\"authors\":\"Zhelong Wang, Hongyu Zhao, Hong Shang, S. Qiu\",\"doi\":\"10.1109/IST.2012.6295576\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In order to improve the accuracy and robustness of real-time tracking system, this paper presents new methods for efficient object tracking in video sequences using multiple features and particle filter. Based on the problem that tracking with a single feature is susceptible to interference, the color and edge orientation features are combined under the particle filtering framework, and an adaptive feature-weight assignment approach is also proposed in the process of feature fusion. In the prediction period of particle filter algorithm, the mean-shift method is used to improve the particle swarm optimization algorithm. In this way, the number of effective particles is increased and the real-time performance of the tracking system is improved. Experiment results show that the proposed tracking system is more accurate and more efficient than the traditional color feature based mean-shift algorithm.\",\"PeriodicalId\":213330,\"journal\":{\"name\":\"2012 IEEE International Conference on Imaging Systems and Techniques Proceedings\",\"volume\":\"43 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-07-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE International Conference on Imaging Systems and Techniques Proceedings\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IST.2012.6295576\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE International Conference on Imaging Systems and Techniques Proceedings","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IST.2012.6295576","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An improved particle filter for multi-feature tracking application
In order to improve the accuracy and robustness of real-time tracking system, this paper presents new methods for efficient object tracking in video sequences using multiple features and particle filter. Based on the problem that tracking with a single feature is susceptible to interference, the color and edge orientation features are combined under the particle filtering framework, and an adaptive feature-weight assignment approach is also proposed in the process of feature fusion. In the prediction period of particle filter algorithm, the mean-shift method is used to improve the particle swarm optimization algorithm. In this way, the number of effective particles is increased and the real-time performance of the tracking system is improved. Experiment results show that the proposed tracking system is more accurate and more efficient than the traditional color feature based mean-shift algorithm.