Context Recovery and Knowledge Retrieval: A Novel Two-Stream Framework for Video Anomaly Detection

TL;DR

This paper introduces a two-stream framework combining context recovery and knowledge retrieval for video anomaly detection, improving accuracy by leveraging motion prediction and normality knowledge comparison.

Contribution

It proposes a novel two-stream approach with a spatiotemporal U-Net and an improved hashing method, enhancing anomaly detection by integrating local context and learned normality knowledge.

Findings

Achieves state-of-the-art results on four datasets.

Demonstrates the effectiveness of combining two complementary streams.

Improves anomaly detection accuracy over existing methods.

Abstract

Video anomaly detection aims to find the events in a video that do not conform to the expected behavior. The prevalent methods mainly detect anomalies by snippet reconstruction or future frame prediction error. However, the error is highly dependent on the local context of the current snippet and lacks the understanding of normality. To address this issue, we propose to detect anomalous events not only by the local context, but also according to the consistency between the testing event and the knowledge about normality from the training data. Concretely, we propose a novel two-stream framework based on context recovery and knowledge retrieval, where the two streams can complement each other. For the context recovery stream, we propose a spatiotemporal U-Net which can fully utilize the motion information to predict the future frame. Furthermore, we propose a maximum local error mechanism to alleviate the problem of large recovery errors caused by complex foreground objects. For the knowledge retrieval stream, we propose an improved learnable locality-sensitive hashing, which optimizes hash functions via a Siamese network and a mutual difference loss. The knowledge about normality is encoded and stored in hash tables, and the distance between the testing event and the knowledge representation is used to reveal the probability of anomaly. Finally, we fuse the anomaly scores from the two streams to detect anomalies. Extensive experiments demonstrate the effectiveness and complementarity of the two streams, whereby the proposed two-stream framework achieves state-of-the-art performance on four datasets.