应用于运动视频目标跟踪的改进粒子滤波模型技术研究
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摘要
粒子滤波作为目标跟踪的主流技术,在人体运动视频分析中具有广阔的应用前景。为了进一步提高目标追踪的精度,提出一种基于改进粒子滤波模型的运动视频目标跟踪算法。采用HSV分布模型构建目标观测模型,结合粒子滤波器和退化权值检测运动目标是否出现在目标观测模型中。最后引入遗传算法对粒子滤波算法进行改进,以便消除粒子退化的现象。在体育运动员视频中进行测试验证,实验结果表明,提出的算法能够有效完成运动视频中的人体目标跟踪,与其他算法相比,提出算法的精度和运行效率更高。
As the mainstream technology of target tracking,particle filtering has broad application prospect in human motion video analysis. A motion video target tracking algorithm based on improved particle filtering model is proposed to further improve the accuracy of target tracking. The target observation model is constructed by using HSV distribution model,and then the particle filter and degradation weight are combined to detect whether the moving target appears in the target observation model. The genetic algorithm is introduced to improve the particle filtering algorithm,and eliminate the phenomenon of particle degradation. The test verification was conducted with the sports athlete video. The experimental results show that the proposed algorithm can effectively complete the human target tracking in motion video,and has higher accuracy and operation efficiency than other algorithms.
As the mainstream technology of target tracking,particle filtering has broad application prospect in human motion video analysis. A motion video target tracking algorithm based on improved particle filtering model is proposed to further improve the accuracy of target tracking. The target observation model is constructed by using HSV distribution model,and then the particle filter and degradation weight are combined to detect whether the moving target appears in the target observation model. The genetic algorithm is introduced to improve the particle filtering algorithm,and eliminate the phenomenon of particle degradation. The test verification was conducted with the sports athlete video. The experimental results show that the proposed algorithm can effectively complete the human target tracking in motion video,and has higher accuracy and operation efficiency than other algorithms.
引文
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[10]吕韵秋,刘凯,费聚锋,等.基于压缩跟踪和遗传算法的实时跟踪方法[J].制导与引信,2016,37(4):34-39.LüYunqiu,LIU Kai,FEI Jufeng,et al. Real-time tracking method based on compression tracking and genetic algorithm[J]. Guidance&fuze,2016,37(4):34-39.
[11]刘峰,宣士斌,刘香品.基于云自适应粒子群优化粒子滤波的视频目标跟踪[J].数据采集与处理,2015(2):452-463.LIU Feng,XUAN Shibin,LIU Xiangpin. Video target tracking based on cloud adaptive particle swarm optimization particle filter[J]. Data acquisition and processing,2015(2):452-463.
[2] DEMIGHA O,HIDOUCI W K,AHMED T. On energy efficiency in collaborative target tracking in wireless sensor network:a review[J]. IEEE communications surveys&tutorials,2013,15(3):1210-1222.
[3] YANG B,NEVATIA R. Multi-target tracking by online learning a CRF model of appearance and motion patterns[J]. International journal of computer vision,2014,107(2):203-217.
[4]侯一民,贺子龙.嵌入Mean Shift的粒子滤波目标跟踪算法[J].计算机系统应用,2012,21(12):80-84.HOU Yimin,HE Zilong. Particle filter target tracking algorithm embedded in Mean Shift[J]. Computer systems and applications,2012,21(12):80-84.
[5] BIAN L,LI T,WEI Y,et al. Improved particle filtering target tracking algorithm for HLBP and color feature adaptive fusion[J]. Journal of Nanjing Normal University,2018(1):45-49.
[6] QUANG P B,MUSSO C,GLAND F L. Particle filtering and the Laplace method for target tracking[J]. IEEE transactions on aerospace&electronic systems,2016,52(1):350-366.
[7] ZHU S,WANG D,CHANG B L. Ground target tracking using UAV with input constraints[J]. Journal of intelligent&robotic systems theory&applications,2013,69(1):417-429.
[8] HOANG H G,BA T V. Sensor management for multi-target tracking via multi-Bernoulli filtering[J]. Automatica,2014,50(4):1135-1142.
[9] CHEN Y. Target tracking feature selection algorithm based on Adaboost[J]. Telkomnika Indonesian journal of electrical engineering,2014,12(1):734-740.
[10]吕韵秋,刘凯,费聚锋,等.基于压缩跟踪和遗传算法的实时跟踪方法[J].制导与引信,2016,37(4):34-39.LüYunqiu,LIU Kai,FEI Jufeng,et al. Real-time tracking method based on compression tracking and genetic algorithm[J]. Guidance&fuze,2016,37(4):34-39.
[11]刘峰,宣士斌,刘香品.基于云自适应粒子群优化粒子滤波的视频目标跟踪[J].数据采集与处理,2015(2):452-463.LIU Feng,XUAN Shibin,LIU Xiangpin. Video target tracking based on cloud adaptive particle swarm optimization particle filter[J]. Data acquisition and processing,2015(2):452-463.