自适应空间异常的目标跟踪

Adaptive Spatial and Anomaly Target Tracking

为了解决判别式空间正则项的目标跟踪算法在遮挡、旋转等因素干扰下失跟率较高的问题,该文提出一种自适应空间异常的目标跟踪算法。首先,在目标函数中加入自适应空间正则项,既缓解了边界效应带来的影响,又提高了滤波器对目标和背景区域的分辨能力;其次,根据每一帧的响应值计算验证分数,分析跟踪结果的可信度和异常情况;最后为目标模型和响应图模型的更新速率实现动态取值。大量实验结果表明,自适应空间异常的目标跟踪算法能够较好地处理背景模糊、形状变化等多种异常情况,具有较高的跟踪性能。

In order to solve the problem that the target tracking algorithm based on the discriminant spatial regularization term has a high mistracking rate under the interference of occlusion,rotation and other factors,an adaptive spatial and anomaly target tracking is proposed.Firstly,an adaptive spatial regularization term is constructed in the objective function,which not only alleviates the influence of boundary effect,but also improves the resolution of the filter between the target and the background region.Secondly,the verification score is calculated according to the response value of each frame,and the reliability and abnormality of the tracking results are analyzed.Finally,the updating rate of target model and response model is dynamically evaluated.A large number of experimental results show that the target tracking algorithm based on adaptive spatial anomaly can deal with background blur,shape change and other abnormal situations well,and has robust tracking performance.