Video Copy Detection Based on Spatio-Temporal Trajectory Behavior Feature

Large scale video copy detection is to detect copied segments of provided video content from large video databases. This application requires compact feature which is insensitive to various visual copy changes. However, traditional image features are prone to spatial changes such as color and texture transformations. The reason is the copied versions have large image transformations including global quality decrease and local visual distortions, which dramatically change the distribution of visual features. Consequently, previous methods based on histogram features and ordinal measures failed in copy detection. To solve this problem, this paper proposes the use of invariant visual features based on keypoint trajectory behavior. Instead of using the spatial cues, the proposed approach models the temporal information as robust features. Temporal cues are quantified based on keypoint trajectories which are insensitive to strong changes. Then videos are represented by spatio-temporal features which are more robust to copy changes. Bag of trajectory (BoT) technigue is adopted for fast pattern matching in large database. The experimental results show that spatio-temporal trajectory features are robust to various visual changes, including image blur, scale and ratio changes, and minor frame rate change. Compared with the state-of-art scheme using ordinal measure, the proposed algorithm with lower cost presents better accuracy.