Online visual tracking via correlation filter with convolutional networks

Zheng Li, Jianfei Yang, Juan Zha, Chang-Dong Wang, Wei-Shi Zheng

Abstract: Robust online visual tracking is a challenging task of the computer vision due to its violent variation within the video sequences. To approach these issues, deep networks have been applied in order to improve accuracy and correlation filter based trackers perform excellent efficiency and adaptation to scale. In this paper, we present a novel method with convolutional networks and correlation filter. A simple two-layer convolutional network is constructed to learn robust representations, which encode the inner geometric layout and local structural information, and the tracking framework resorts to learning discriminative correlation filters based on them. For our method satisfies both veracity and efficiency by finding a compromise between two theories, it performs favorably against several state-of-the-art methods with 50 public challenging videos.