Anomaly Detection in Video Sequence With Appearance-Motion Correspondence

By Trong-Nguyen Nguyen, Jean Meunier. IEEE International Conference on Computer Vision, 2019

421 citations recorded by the source corpus.

Anomaly detection in surveillance videos is currently a challenge because of the diversity of possible events. We propose a deep convolutional neural network (CNN) that addresses this problem by learning a correspondence between common object appearances (e.g. pedestrian, background, tree, etc.) and their associated motions. Our model is designed as a combination of a reconstruction network and an image translation model that share the same encoder. The former sub-network determines the most significant structures that appear in video frames and the latter one attempts to associate motion templates to such structures. The training stage is performed using only videos of normal events and the model is then capable to estimate frame-level scores for an unknown input. The experiments on 6 benchmark datasets demonstrate the competitive performance of the proposed approach with respect to state-of-the-art methods.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/53599f3748b73f5d3bbddab646905b5b8e7d3210

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Citations in the corpus

A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Prediction (2021, methodology): “For example, an AE comprising a shared encoder and two separate decoders is proposed in [36], to reconstruct frame and predict flow separately.”
Anomaly Detection in Video via Self-Supervised and Multi-Task Learning (2020, methodology): “Frame-level AUC scores (in %) of the state-of-the-art methods [7, 8, 9, 10, 13, 14, 16, 17, 18, 19, 20, 21, 23, 24, 27, 28, 29, 30, 31, 32, 33, 34, 36, 37, 38, 40, 41, 47, 48, 49, 51, 53, 55, 57, 59, 60, 61, 62, 64] versus our deep+wide architecture trained on four proxy tasks at the object level, a”
Learning Memory-Guided Normality for Anomaly Detection (2020, neutral): “2 AMC [31] 96.”
Mixed supervision for surface-defect detection: from weakly to fully supervised learning (2021, result): “We also opted to use normalization of the scoring similar to [23], which slightly improved results on noisy outputs.”