Robust compressive tracking based on SURF

Recently, more and more researchers focus on compressive tracking due to its simple and efficient performance. However, performance of classical compressive tracking algorithms are very weak in illumination change, rotation and motion blur, which produce the drift problems easily in object tracking. Therefore, we present a kind of compressive tracking algorithm based on SURF. Firstly, we extract the features for the appearancemodel from foreground and background of the target as the positive and negative samples using SURF; then, we compress the high-dimensional feature space to low-dimensional space on the basis of compressive sensing theory. Secondly, we classify the positive and negative samples according to naive Bayes classifier with online update. The experiments show that the method we proposed performs effective and efficient and runs in real time with an average of 27 fps on challenging test sequences.