Fast object detection based on several samples by training voting space

In this paper, we propose a fast and novel detection method based on several samples to localize objects in target images or video. Firstly, we use several samples to train a voting space which is constructed by cells at corresponding positions. Each cell is described by a Gaussian distribution whose parameters are estimated by maximum likelihood estimation method. Then, we randomly choose one sample as a query image. Patches of target image are recognized by densely voting in the trained voting space. Next, we use a mean-shift method to refine multiple instances of object class. The high performance of our approach is demonstrated on several challenging data sets in both efficiency and effectiveness.