Correlation Filter With Motion Detection for Robust Tracking of Shape-Deformed Targets

• Main Authors: Chengyuan Liu, Jianglei Gong, Jiang Zhu, Jinxin Zhang, Yunyi Yan
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Robust target tracking; shape-deformed target; correlation filter; motion detection
• Online Access: https://ieeexplore.ieee.org/document/9090888/

Target tracking is an important area of research in computer vision where stable target's tracking has been well solved. But in real world, it is difficult to ensure that the camera or lens could be fixed and the target could maintain its shape in whole video sequence. And as a result, in these unstable cases, robust tracking algorithms have to deal with the problem of target shape-deforming. Once the scenes video sequence contains shape-deformed target, tracking become a real challenging problem. Most previous tracking algorithms based on craft features only used HOG or/and CN features. This paper proposed an algorithm named as Correlation Filtering with Motion Detection (CFMD). This algorithm takes into account the camera shake and target motion information of the video sequence. After removing the effects of lens shake and camera movement, this algorithm can predict the motion information of the target, thereby effectively improving the tracking accuracy and robustness. In CFMD, the target position is determined by the weighted outputs of motion detection and correlation filter tracker. We evaluated our CMFD algorithm on the OTB-100 and VOT-2018 dataset compared with other target tracking algorithms, including Kernel Correlation Filter (KCF), Scale Adaptive with Multiple Features tracker (SAMF), Discriminative Scale Space Tracker (DSST), and Sum of Template and Pixel-wise LEarners (Staple), Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking(STRCF), Multi-Cue Correlation Filters for Robust Visual Tracking(MCCT). The experimental results showed that our algorithm owns the property of robust tracking of shape-deformed targets in video sequences containing lens shaking or camera moving and it achieves the state-of-the-art precision and tracking effects.