基于上下文感知和尺度自适应的实时目标跟踪

Real-time Object Tracking Based on Context Awareness and Scale Adaptation

针对传统核相关滤波视觉目标跟踪算法在快速运动、背景杂波、运动模糊等情况下跟踪精度低且不能处理尺度变化的问题,提出了一种基于上下文感知和尺度自适应的实时目标跟踪算法。该算法在核相关滤波算法框架的基础上,引入了上下文感知和尺度自适应方法,增加了背景信息且能够处理目标的尺度变化。首先,利用融合了fHOG(fusion histogram of oriented gradient)、CN(color names)和灰度的特征对目标区域进行采样,训练一个二维位移滤波器,然后,在目标区域建立尺度金字塔,利用fHOG对目标区域进行多尺度采样,训练一个一维尺度滤波器,最后,在模型更新阶段改进了更新策略。在标准数据集OTB-2015上对100组视频序列进行的试验结果表明,提出的算法比基准算法(kernel correlation filter,KCF)精度提高了13.9%,成功率提高了14.2%,且优于实验中对比的其他跟踪算法。在尺度变化、运动模糊、快速运动等条件下,提出的算法在准确跟踪的同时,能够保持较高的速度。

Because the traditional kernel correlation filter algorithm for visual object tracking has low tracking accuracy under fast motion,background clutter,and motion blurring conditions and cannot deal with scale changes,a real-time object tracking algorithm based on context awareness and scale adaptation is proposed.Based on the kernel correlation filter algorithm framework,context-aware and scale-adaptive methods are introduced to add background information and handle changes in the scale of the target.First,the target region is sampled using the features of the fusion histogram of oriented gradient(fHOG),color names(CN),and gray,and a two-dimensional translation filter is trained.Then,a scale pyramid is established in the target area and multi-scale sampling is performed using fHOG on the target area.Following this,a one-dimensional scale filter is trained.Finally,the update strategy is improved in the model updating stage.The experimental results of 100 sets of video sequences in the standard OTB-2015 dataset show that the proposed algorithm showed an improvement in the accuracy by 13.9%as compared with the benchmark algorithm(kernel correlation filter,KCF),and the success rate improved by 14.2%,which is superior to that of other comparison-tracking algorithms considered in the experiment.Under the conditions of scale change,motion blur,and fast motion,the proposed algorithm can maintain a high speed with accurate tracking.