基于混合特征的运动目标跟踪方法

Robust Visual Tracking Based on Mixed Features

为了应对运动目标跟踪任务中目标的尺度、光照变化和形变等情况,提出了一种基于混合特征的运动目标跟踪方法——So H-DLT,综合考虑了运动目标的轮廓特征和细节特征.在粒子滤波跟踪过程中引入方向直方图描述目标轮廓特征,保证与目标最相似的粒子在尺度、光照变化和形变的情况下仍能获得较高的置信度,并作为跟踪结果输出.结合深度学习获得的高层特征和具有尺度不变性的加速鲁棒特征计算粒子权重,提高了复杂运动场景下目标跟踪的准确度,强化了So H-DLT方法对尺度变化运动目标跟踪的鲁棒性.实验结果表明,So H-DLT与其他方法相比获得了更好的跟踪效果.

In order to deal with scale variation,illumination changes and deformation of the target in tracking tasks,a visual tracking algorithm based on mixed features,called So H-DLT,was proposed,considering both the contour features and detail features. Orientation histogram is introduced to describe the contour features of candidate samples in the process of particle filter,ensuring that the particle which is the most similar with the target can still get a high degree of confidence and can be output as the result of tracking in the case of scale,illumination changes and deformation. Speed-up robust features（ SURF）feature and high-level features from deep learning are integrated to calculate the weights of particles,improving the tracking accuracy in complex scenes and enhancing the robustness of So H-DLT to scale variation. Experiments show that So H-DLT algorithm has better tracking performance than the contrast algorithms in both quantitative and qualitative evaluation.