劉懿
關鍵詞: 目標跟蹤; 遺傳算法; 運動視頻; 粒子濾波; HSV分布模型; 退化權值
中圖分類號: TN713?34; TP391 文獻標識碼: A 文章編號: 1004?373X(2019)03?0065?03
Abstract: 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.
Keywords: target tracking; genetic algorithm; motion video; particle filtering; HSV distribution model; degeneration weight0 引 言
目標跟蹤技術最開始應用于軍事領域,並逐漸在民用領域得到快速的推廣。目標跟蹤技術能夠觀測被跟蹤目標的屬性與狀態,從而獲取被跟蹤目標在不同時刻的變化。通過分析這些變化能夠對目標實現位置跟蹤[1?2]。一般來說,視頻目標跟蹤需要對圖像序列進行分析以便完成對運動目標的檢測,包括目標的提取、識別和跟蹤,從而得到跟蹤目標的各項運動參數,如加速度、速度、位置等[3]。
如何實現複雜背景下運動目標的準確跟蹤一直是科研人員研究的熱點問題。基於蒙特卡羅思想的粒子濾波算法一直廣泛應用於各種非線性及非高斯系統,可以有效應用於目標跟蹤。因此,針對運動視頻目標跟蹤問題,本文提出一種基於改進粒子濾波模型的運動視頻目標跟蹤算法。利用運動員視頻進行具體測試,結果顯示在無任何先驗信息的情況下,提出的算法能夠較好地跟蹤運行視頻中的人體目標,驗證了其可行性和先進性。1 相關研究
文獻[4]提出一種基於嵌入Mean?Shift的粒子濾波目標跟蹤。文獻[5]提出面向顏色特徵自適應融合的改進粒子濾波目標跟蹤算法。文獻[6]提出基於粒子濾波和拉普拉斯方法的目標跟蹤技術。以上幾種方法均採用混合優化策略,通過將先進的優化算法和粒子濾波算法進行結合來提高目標跟蹤的性能,以便彌補粒子濾波算法的缺陷。遺傳算法作為一種仿生進化式算法,其基本理念是適者生存規則和種群進化,具有全局搜索能力高和前期收斂速度快的特點,可用於消除粒子退化問題。因此,本文引入遺傳算法對粒子濾波算法進行改進,以便增加粒子的多樣性,從而消除粒子退化的現象。此外,採用HSV分布模型構建目標觀測模型,然後結合粒子濾波器和退化權值檢測運動目標是否出現在目標觀測模型中。
對三種跟蹤算法進行測試,結果如表1所示。從表1可以看出,提出的方法明顯優於其他兩種方法,其平均誤差精度一直維持在比較低的水平。在測試的視頻序列中,本文提出的跟蹤算法、標準粒子濾波算法、Mean?Shift粒子濾波算法的平均誤差分別為18.89,24.71,36.42。
4 結 論
本文提出一種基於改進粒子濾波模型的運動視頻目標跟蹤算法。首先採用HSV分布模型構建目標觀測模型,然後結合粒子濾波器和退化權值來檢測運動目標是否出現在目標觀測模型中。最後引入遺傳算法對粒子濾波算法進行改進,以便消除粒子退化的現象。利用運動員視頻進行具體測試,結果顯示在無任何先驗信息的情況下,提出算法能夠較好地跟蹤運行視頻中的人體目標,驗證了其可行性和先進性。
參考文獻
[1] MILAN A, SCHINDLER K, ROTH S. Multi?target tracking by discrete?continuous energy minimization [J]. IEEE transactions on pattern analysis & machine intelligence, 2016, 38(10): 2054?2068.
[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 lear?ning a CRF model of appearance and motion patterns [J]. International journal of computer vision, 2014, 107(2): 203?217.
[4] 侯一民,賀子龍.嵌入Mean Shift的粒子濾波目標跟蹤算法[J].計算機系統應用,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] 呂韻秋,劉凱,費聚鋒,等.基於壓縮跟蹤和遺傳算法的實時跟蹤方法[J].制導與引信,2016,37(4):34?39.
L? Yunqiu, 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] 劉峰,宣士斌,劉香品.基於雲自適應粒子群優化粒子濾波的視頻目標跟蹤[J].數據採集與處理,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.
關鍵詞: 目標跟蹤; 遺傳算法; 運動視頻; 粒子濾波; HSV分布模型; 退化權值
中圖分類號: TN713?34; TP391 文獻標識碼: A 文章編號: 1004?373X(2019)03?0065?03
Abstract: 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.
Keywords: target tracking; genetic algorithm; motion video; particle filtering; HSV distribution model; degeneration weight0 引 言
目標跟蹤技術最開始應用于軍事領域,並逐漸在民用領域得到快速的推廣。目標跟蹤技術能夠觀測被跟蹤目標的屬性與狀態,從而獲取被跟蹤目標在不同時刻的變化。通過分析這些變化能夠對目標實現位置跟蹤[1?2]。一般來說,視頻目標跟蹤需要對圖像序列進行分析以便完成對運動目標的檢測,包括目標的提取、識別和跟蹤,從而得到跟蹤目標的各項運動參數,如加速度、速度、位置等[3]。
如何實現複雜背景下運動目標的準確跟蹤一直是科研人員研究的熱點問題。基於蒙特卡羅思想的粒子濾波算法一直廣泛應用於各種非線性及非高斯系統,可以有效應用於目標跟蹤。因此,針對運動視頻目標跟蹤問題,本文提出一種基於改進粒子濾波模型的運動視頻目標跟蹤算法。利用運動員視頻進行具體測試,結果顯示在無任何先驗信息的情況下,提出的算法能夠較好地跟蹤運行視頻中的人體目標,驗證了其可行性和先進性。1 相關研究
文獻[4]提出一種基於嵌入Mean?Shift的粒子濾波目標跟蹤。文獻[5]提出面向顏色特徵自適應融合的改進粒子濾波目標跟蹤算法。文獻[6]提出基於粒子濾波和拉普拉斯方法的目標跟蹤技術。以上幾種方法均採用混合優化策略,通過將先進的優化算法和粒子濾波算法進行結合來提高目標跟蹤的性能,以便彌補粒子濾波算法的缺陷。遺傳算法作為一種仿生進化式算法,其基本理念是適者生存規則和種群進化,具有全局搜索能力高和前期收斂速度快的特點,可用於消除粒子退化問題。因此,本文引入遺傳算法對粒子濾波算法進行改進,以便增加粒子的多樣性,從而消除粒子退化的現象。此外,採用HSV分布模型構建目標觀測模型,然後結合粒子濾波器和退化權值檢測運動目標是否出現在目標觀測模型中。
對三種跟蹤算法進行測試,結果如表1所示。從表1可以看出,提出的方法明顯優於其他兩種方法,其平均誤差精度一直維持在比較低的水平。在測試的視頻序列中,本文提出的跟蹤算法、標準粒子濾波算法、Mean?Shift粒子濾波算法的平均誤差分別為18.89,24.71,36.42。
4 結 論
本文提出一種基於改進粒子濾波模型的運動視頻目標跟蹤算法。首先採用HSV分布模型構建目標觀測模型,然後結合粒子濾波器和退化權值來檢測運動目標是否出現在目標觀測模型中。最後引入遺傳算法對粒子濾波算法進行改進,以便消除粒子退化的現象。利用運動員視頻進行具體測試,結果顯示在無任何先驗信息的情況下,提出算法能夠較好地跟蹤運行視頻中的人體目標,驗證了其可行性和先進性。
參考文獻
[1] MILAN A, SCHINDLER K, ROTH S. Multi?target tracking by discrete?continuous energy minimization [J]. IEEE transactions on pattern analysis & machine intelligence, 2016, 38(10): 2054?2068.
[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 lear?ning a CRF model of appearance and motion patterns [J]. International journal of computer vision, 2014, 107(2): 203?217.
[4] 侯一民,賀子龍.嵌入Mean Shift的粒子濾波目標跟蹤算法[J].計算機系統應用,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] 呂韻秋,劉凱,費聚鋒,等.基於壓縮跟蹤和遺傳算法的實時跟蹤方法[J].制導與引信,2016,37(4):34?39.
L? Yunqiu, 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] 劉峰,宣士斌,劉香品.基於雲自適應粒子群優化粒子濾波的視頻目標跟蹤[J].數據採集與處理,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.
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