1,000 research outputs found
A Hierarchical Spatio-Temporal Graph Convolutional Neural Network for Anomaly Detection in Videos
Deep learning models have been widely used for anomaly detection in
surveillance videos. Typical models are equipped with the capability to
reconstruct normal videos and evaluate the reconstruction errors on anomalous
videos to indicate the extent of abnormalities. However, existing approaches
suffer from two disadvantages. Firstly, they can only encode the movements of
each identity independently, without considering the interactions among
identities which may also indicate anomalies. Secondly, they leverage
inflexible models whose structures are fixed under different scenes, this
configuration disables the understanding of scenes. In this paper, we propose a
Hierarchical Spatio-Temporal Graph Convolutional Neural Network (HSTGCNN) to
address these problems, the HSTGCNN is composed of multiple branches that
correspond to different levels of graph representations. High-level graph
representations encode the trajectories of people and the interactions among
multiple identities while low-level graph representations encode the local body
postures of each person. Furthermore, we propose to weightedly combine multiple
branches that are better at different scenes. An improvement over single-level
graph representations is achieved in this way. An understanding of scenes is
achieved and serves anomaly detection. High-level graph representations are
assigned higher weights to encode moving speed and directions of people in
low-resolution videos while low-level graph representations are assigned higher
weights to encode human skeletons in high-resolution videos. Experimental
results show that the proposed HSTGCNN significantly outperforms current
state-of-the-art models on four benchmark datasets (UCSD Pedestrian,
ShanghaiTech, CUHK Avenue and IITB-Corridor) by using much less learnable
parameters.Comment: Accepted to IEEE Transactions on Circuits and Systems for Video
Technology (T-CSVT
A PatchMatch-based Dense-field Algorithm for Video Copy-Move Detection and Localization
We propose a new algorithm for the reliable detection and localization of
video copy-move forgeries. Discovering well crafted video copy-moves may be
very difficult, especially when some uniform background is copied to occlude
foreground objects. To reliably detect both additive and occlusive copy-moves
we use a dense-field approach, with invariant features that guarantee
robustness to several post-processing operations. To limit complexity, a
suitable video-oriented version of PatchMatch is used, with a multiresolution
search strategy, and a focus on volumes of interest. Performance assessment
relies on a new dataset, designed ad hoc, with realistic copy-moves and a wide
variety of challenging situations. Experimental results show the proposed
method to detect and localize video copy-moves with good accuracy even in
adverse conditions
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