Qiu Chen¹, Koji Kotani², Feifei Lee³ and Tadahiro Ohmi², ¹Kogakuin University, Japan, ²Tohoku University, Japan and ³University of Shanghai for Science and Technology, China

In this paper, we propose a novel fast video search algorithm for large video database. Histogram of Oriented Gradients (HOG) has been reported which can be reliably applied to object detection, especially pedestrian detection. We use HOG based features as a feature vector of a frame image in this study. Combined with active search, a temporal pruning algorithm, fast and robust video search can be achieved. The proposed search algorithm has been evaluated by 6 hours of video to search for given 200 video clips which each length is 15 seconds. Experimental results show the proposed algorithm can detect the similar video clip more accurately and robust against Gaussian noise than conventional fast video search algorithm.

Fast search, Video database, HOG Features