关键词: 尺度变换, 上下文感知, 相关滤波, 帧差均值

Abstract:

In order to solve scale transformation, similar target, occlusion, background noisy and other common problems in target tracking, an adaptive scale context-aware correlation filter tracking algorithm is proposed. In view of the above problems, based on the correlation filter tracking algorithm, the context information around the target is introduced into the classifier as a hard negative sample to enhance the discriminative ability of the classifier. The discriminant scale filter is learned online through the scale pool at the target position and estimate the optimal target size. The target state is evaluated by the mean frame difference and the learning rate of the model update is adaptively adjusted. The experimental results show that the proposed algorithm is robust in fast motion, target deformation and any other scenarios.

Key words: scale transformation, context-aware, correlation filter, mean frame difference