Video Segment Copy Detection Using Memory Constrained Hierarchical Batch-Normalized LSTM Autoencoder
This addresses scalable detection of copied video segments in large databases, particularly against temporal attacks, with incremental improvements over existing methods.
The paper tackles video segment copy detection by proposing a deep learning-based video hashing method that learns variable-length binary hash codes using temporal and spatial features, specifically resilient to time cropping, and demonstrates efficacy on a dataset of around 25,000 videos with segment copies.
In this report, we introduce a video hashing method for scalable video segment copy detection. The objective of video segment copy detection is to find the video (s) present in a large database, one of whose segments (cropped in time) is a (transformed) copy of the given query video. This transformation may be temporal (for example frame dropping, change in frame rate) or spatial (brightness and contrast change, addition of noise etc.) in nature although the primary focus of this report is detecting temporal attacks. The video hashing method proposed by us uses a deep learning neural network to learn variable length binary hash codes for the entire video considering both temporal and spatial features into account. This is in contrast to most existing video hashing methods, as they use conventional image hashing techniques to obtain hash codes for a video after extracting features for every frame or certain key frames, in which case the temporal information present in the video is not exploited. Our hashing method is specifically resilient to time cropping making it extremely useful in video segment copy detection. Experimental results obtained on the large augmented dataset consisting of around 25,000 videos with segment copies demonstrate the efficacy of our proposed video hashing method.