Multi-Channel Features Spatio-Temporal Context Learning for Visual Tracking

Abstract

Visual tracking is a challenging issue in surveillance, human-computer Interaction, and intelligent robotics, among others. Managing appearance changes of the target object, illumination changes, rotations, non-rigid deformations, partial or full occlusions, background clutter, fast motion, and so forth is generally difficult. Among the numerous existing trackers, the correlation-filter-based tracker can achieve appealing performance with a fast speed for fast Fourier transform. Motivated by this property, the spatiotemporal context (STC) learning algorithm was proposed with the consideration of the information from the context around the target, and this algorithm achieved good results. However, STC only utilizes the overall intensity information. In this paper, we propose a multi-channel features STC learning algorithm with an improved scale-adaptive scheme. Our algorithm integrates powerful features, including Histogram of Oriented Gradients and color naming, using kernel methods on the basis of the STC algorithm to further enhance the overall tracking performance. Extensive experimental results obtained from various benchmark data sets demonstrate that the proposed tracker is promising for various challenging scenarios and maintains real-time performance at an average speed of 78 frames/s. According to the test results, our algorithm outperforms the STC algorithm and achieves performance that is competitive with the state-of-the-art algorithms.