On-line boosting based real-time tracking with efficient HOG

Abstract

In this paper, a real-time visual tracking system that delivers superior performance under difficult situations is proposed. The system is based on Histogram of Oriented Gradient (HOG) within the on-line boosting framework. For environmental adaptation, the HOG feature is calculated with blocks of random scale, position and aspect ratio which form a feature pool. The on-line boosting can then select the best distinguishable features from this pool for the robust tracking. The randomness of the blocks guarantees the existence of those features. Three experiments are conducted to highlight different characteristics of this new system. The first experiment proves the validity for the system to be able to pick out the best possible HOG features. The second experiment shows its robustness against bad illuminations and small foreground background difference. The third experiment demonstrates its advancement compared with the Haar-based state-of-the-art system. All those are offered without sacrificing the computation load.