Video copy detection using histogram based spatio-temporal features

We propose a robust video copy detection method in this paper, which using combined histogram-based Spatiotemporal features for massive video database. The first is based on Histogram of Oriented Gradients (HOG) descriptor, an effective descriptor for object detection. It is used for describing the global feature of a frame in video sequence. The second is based on ordinal measure representation which is robust to size variation and color shifting as temporal feature. Furthermore, by adding an active search algorithm, the spatio-temporal features are combined to achieve video copy detection fast and accurately. Experiments show that our approach outperforms traditional algorithms in running time and detection accuracy.