Real-Time Long-Term Tracking With Prediction-Detection-Correction
Real-time long-term visual tracking is one of the most challenging problems in computer vision due to various factors such as occlusion and motion ambiguity. To achieve robust long-term tracking, most state-of-the-art methods typically construct an online detector in each frame. However, they fail to achieve real-time performance due to high computational complexity. In this paper, we propose a novel real-time long-term tracking algorithm by exploiting a joint Prediction-Detection-Correction Tracking framework (PDCT). We utilize a superpixel optical flow to construct a predictor to estimate the target motion and internal scale variation. To locate the target at a finer level, we develop an improved kernelized correlation detector with an adaptive online learning rate and translation-scale parameters from the predictor. To refine the tracking result and redetect the target in the case of a tracking failure, we devise a corrector utilizing dual online SVMs with dense sampling and reliable history samples. The SVMs are trained with passive-aggressive learning and online retraining strategies. In addition, we employ a selection mechanism for the correlation responses to maintain reliable samples effectively. As a result, our proposed tracker is able to refine tracking results via the corrector and detector and maintains reliable tracking results for subsequent tracking. Extensive experiments on the widely used object tracking benchmark show that the proposed tracker is superior to state-of-the-art trackers in terms of both effectiveness and efficiency, and the integration of each component is effective under the PDCT framework.