Accurate visual tracking via reliable patch

Abstract

In this paper, we propose a novel visual tracking framework that intelligently discovers reliable patterns from a wide range of video to resist drift error for long-term tracking tasks. First, we design a Discrete Fourier Transform (DFT) based tracker which is able to exploit a large number of tracked samples while still ensures real-time performance. Second, we propose a clustering method with temporal constraints to explore and memorize consistent patterns from previous frames, named as "reliable memories". By virtue of this method, our tracker can utilize uncontaminated information to alleviate drifting issues. Experimental results show that our tracker performs favorably against other stateof-the-art methods on benchmark datasets. Furthermore, it is significantly competent in handling drifts and able to robustly track challenging long videos over 4000 frames, while most of others lose track at early frames.

Position reset and hybrid feature based particle filter tracking for large-size and long-term full occlusion

“…Ma [12] proposed the robust particle filter tracker based on component analysis for infrared target tracking where the color feature is lack. Yang [13] proposed the reliable patch-based particle filter tracking with motion trajectory prediction and scale estimation for better robustness for target changing.…”