摘要
针对目标跟踪算法在光照变化、背景干扰、目标形变及遮挡时出现的跟踪稳定性下降甚至失败的问题,提出了一种采用时空上下文的抗遮挡实时目标跟踪算法。首先,在时空上下文模型框架下采用自适应降维的颜色特征构建目标外观模型,提高算法在复杂场景中对目标的辨别能力;然后,联合置信图响应的峰值和峰值旁瓣比对目标跟踪的状态进行评估;接着,利用目标模板之间相关系数的变化进一步判断目标是否被严重遮挡;最后,当目标跟踪出现波动时,降低目标模型更新速度,并通过Kalman滤波修正目标位置,当目标被严重遮挡时,则根据Kalman滤波预测目标位置,同时停止更新目标模型,在脱离遮挡后重新捕获目标并进行跟踪。选取了36组具有多种挑战因素的彩色视频序列测试算法的跟踪性能,并与其他表现优异的目标跟踪算法进行了对比分析。实验结果表明,所提算法具有较强的抗遮挡能力,并且在光照变化、背景干扰和目标形变等不利因素影响下仍具有较好的跟踪鲁棒性,同时能够满足目标跟踪的实时性要求。
An anti-occlusion real time target tracking algorithm employing spatio-temporal context was proposed to solve the problems of tracking instability or even failure,which were caused by illumination variation,background clutters,target deformation or occlusion.Firstly,in the framework of spatio-temporal context model,the adaptive dimensionality reduced color features were adopted to describe the target to promote the distinguish ability in complex scene.Secondly,the peak and the peak-to-sidelobe ratio of confidence map response were combined to evaluate the target tracking status.Then,occlusion was discriminated by the correlation coefficient between target templates.Finally,when the target tracking status fluctuated,the update speed of target model was reduced,and the target coordinates were corrected by the Kalman filter.When the target was occluded seriously,the target coordinates was predicted according to the Kalman filter,and the target model was stopped to update for recapturing and tracking the target again after occlusion released.36 color sequences with multiple challenging attributes were selected to evaluate the performance of the proposed algorithm,and it was compared with other excellent target tracking algorithms.The experimental results demonstrated that this algorithm has strong anti-occlusion ability,and improved the robustness of target tracking effectively under the influence of disturbance factors such as illumination variation,background clutters and target deformation.Meanwhile,it met the real time requirement of target tracking.
作者
陈法领
丁庆海
罗海波
惠斌
常铮
刘云鹏
Chen Faling;Ding Qinghai;Luo Haibo;Hui Bin;Chang Zheng;Liu Yunpeng(Shenyang Institute of Automation,Chinese Academy of Sciences,Shenyang 110016,China;Institutes for Robotics and Intelligent Manufacturing,Chinese Academy of Sciences,Shenyang 110169,China;University of Chinese Academy of Sciences,Beijing 100049,China;Key Laboratory of Opto-Electronic Information Processing,Chinese Academy of Sciences,Shenyang 110016,China;The Key Lab of Image Understanding and Computer Vision,Shenyang 110016,China;Space Star Technology Co.,LTD,Beijing 100086,China)
出处
《红外与激光工程》
EI
CSCD
北大核心
2021年第1期325-335,共11页
Infrared and Laser Engineering
基金
国家重点研发计划(2018YFC080192)
中国科学院国防科技创新重点基金(Y8K4160401)。
关键词
目标跟踪
时空上下文
目标跟踪置信度
遮挡判别
KALMAN滤波
target tracking
spatio-temporal context
target tracking confidence
occlusion discrimination
Kalman filter