Enhanced fast compressive tracking based on adaptive measurement matrix

作者：Yun Gao, Hao Zhou, Xuejie Zhang · 发表于：IET Computer Vision · 年份：2015 · DOI：10.1049/iet-cvi.2014.0431 · 被引用次数：15 · 研究领域：Sparse and Compressive Sensing Techniques、Indoor and Outdoor Localization Technologies、Video Surveillance and Tracking Methods

Robust object tracking is a challenging task because of factors such as pose variation, illumination changes, abrupt motion and background clutter across the video sequence. With the introduction of the compressive sensing theory, researchers are provided with a new and effective way of real‐time object tracking. In this study, an enhanced fast compressive tracking based on an adaptive measurement matrix is presented, which the authors have named ‘adaptive fast compressive tracking’ (AFCT). The sparsity of the matrix and the number of columns are adaptively determined according to the dimension of the Haar‐like feature. This measurement matrix is fixed once it has been calculated when selecting a tracked rectangle region in the first frame. Unlike most of the existing compressive trackers, the proposed method adopts a different adaptive measurement matrix for a different targeting object. Compared with the fast compressive tracking (FCT), each measurement element contains more information for the original signal. As a result, stable object tracking is achieved by using fewer measurement elements. The proposed AFCT method can run in real time and outperforms FCT on many challenging video sequences in terms of efficiency, accuracy and robustness.