Real-Time Object Tracking Using Dynamic Measurement Matrix

Object tracking has attracted a lot of attention over the past decades. Features represent the main and primary information of object, however, fixed and invariable feature extraction methods would make the features losing their representation. In this paper, we propose a novel robust single object tracking approach using Dynamic Measurement Matrix to extract dynamic features. In particular, we employ the dynamic measurement matrix to adaptively extract features for discriminating object and background so that features have better and clear representativeness. In additional, our approach is a tracking-by-detection approach via a Naive Bayes Classifier with online updating. Compared to traditional approaches, we not only utilize a Naive Bayes Classifier to classify samples but exploit the nature of this classifier to weigh each compressive feature unit, which would be used to update the measurement matrix. The proposed approach runs in real-time and is robust to pose variation, illumination change and occlusion. Furthermore, both quantitative and qualitative experiments results show that our approach has more stable and superior performance.