关键词: 目标跟踪, 多特征融合, 背景约束, 记忆滤波器, 卷积特征

Abstract:

An object tracking method, which is based on background constraints and convolutional features（TBCCF）, is proposed to solve the target loss problem caused by background aliasing and occlusion in object tracking. Firstly, the feature of input image is fused and dimensionally reduced to enhance the performance of target feature discrimination and reduce the complexity of feature computation. Secondly, background constraints are introduced into the filter training process, which makes the filter more focused on the target response to improve the anti-jamming ability. Finally, by setting memory filter and the peak to sidelobe ratio detection, the tracker can judge whether the target is missing or not. If the target is lost, a convolutional features filter is introduced to re-detect the target. The experimental results of 50 complex scene video sequences on Visual Tracking Benchmark datasets show that the proposed algorithm has better overall accuracy and overall success rate than most existing tracking algorithms.

Key words: object tracking, multi-feature fusion, background constraint, memory filter, convolutional features