Robust Human Detection Using Multiple Scale of Cell Based Histogram of Oriented Gradients and AdaBoost Learning

Human detection is an important task in many applications such as intelligent transport systems, surveillance systems, automatic human assistance systems, image retrieval, and so on. This paper proposes a multiple scale of cell based Histogram of Oriented Gradients (HOG) features description for human detection system. Using these proposed feature descriptors, a robust system is developed according to decision tree structure of boosting algorithm. In this system, the integral image based method is utilized to compute feature descriptors rapidly, and then cascade classifiers are taken into account to reduce computational cost. The experiments were performed on INRIA’s database and our own database, which includes samples in several different sizes. The experiment results showed that our proposed method produce high performance with lower false positive and higher recall rate than the standard HOG features description. This method is also efficient with different resolution and gesture poses under a variety of backgrounds, lighting, as well as individual human in crowds, and partial occlusions.