A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Prediction (2108.06852v1)

Abstract: In this paper, we propose $\text{HF}^2$-VAD, a Hybrid framework that integrates Flow reconstruction and Frame prediction seamlessly to handle Video Anomaly Detection. Firstly, we design the network of ML-MemAE-SC (Multi-Level Memory modules in an Autoencoder with Skip Connections) to memorize normal patterns for optical flow reconstruction so that abnormal events can be sensitively identified with larger flow reconstruction errors. More importantly, conditioned on the reconstructed flows, we then employ a Conditional Variational Autoencoder (CVAE), which captures the high correlation between video frame and optical flow, to predict the next frame given several previous frames. By CVAE, the quality of flow reconstruction essentially influences that of frame prediction. Therefore, poorly reconstructed optical flows of abnormal events further deteriorate the quality of the final predicted future frame, making the anomalies more detectable. Experimental results demonstrate the effectiveness of the proposed method. Code is available at \href{https://github.com/LiUzHiAn/hf2vad}{https://github.com/LiUzHiAn/hf2vad}.

• The paper demonstrates that integrating memory-augmented flow reconstruction with conditional frame prediction enhances video anomaly detection.
• It employs ML-MemAE-SC for reconstructing optical flows and CVAE for predicting future frames, leading to higher AUROC scores.
• Experiments on benchmark datasets like UCSD Ped2, CUHK Avenue, and ShanghaiTech validate its superior performance over state-of-the-art methods.

Hybrid Video Anomaly Detection Framework

The paper, "A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame Prediction," introduces a novel approach to tackle the challenging problem of Video Anomaly Detection (VAD). The method leverages the integration of optical flow reconstruction and frame prediction, formulated as a hybrid framework named HF$^2$-VAD, to enhance the detection of abnormal events in video sequences.

The HF$^2$-VAD framework encompasses two major components: Multi-Level Memory-augmented Autoencoder with Skip Connections (ML-MemAE-SC) and Conditional Variational Autoencoder (CVAE). The ML-MemAE-SC is employed to reconstruct optical flow, aimed at sensitively identifying anomalies through increased flow reconstruction errors. This component incorporates multi-level memory units that store typical normal video patterns at different feature levels, effectively distinguishing unusual activities that deviate from these patterns. Skip connections are integrated to mitigate excessive information compression, crucial for maintaining necessary detail for both reconstruction accuracy and anomaly detection.

Following flow reconstruction, the CVAE model is utilized for predicting future frames, conditioned on both reconstructed flows and previous video frames. This innovative coupling ensures that the quality of optical flow reconstruction directly influences the frame prediction quality. Poorly reconstructed flows around anomalies lead to higher prediction errors, which assist in detecting abnormalities more reliably. The CVAE accomplishes this by maximizing the evidence lower bound (ELBO) to encode the consistency between input frames and flows, driving the accurate generation of expected future video content and enhancing sensitivity to deviations caused by anomalies.

Experimentally, HF$^2$-VAD demonstrates superior performance across multiple standard VAD datasets, such as UCSD Ped2, CUHK Avenue, and ShanghaiTech. The proposed hybrid approach delivers stronger anomaly detection capabilities compared to existing state-of-the-art methods, both within reconstruction-only and prediction-only paradigms, as well as in hybrid methods. This superiority is illustrated by extensive comparative analysis showing higher Area Under the Receiver Operating Characteristic (AUROC) scores, which underscore the efficacy of integrating flow reconstruction and frame prediction seamlessly.

Among the more insightful design aspects, the careful orchestration of memory modules across different levels in ML-MemAE-SC ensures optimal normal pattern memorization. Additionally, employing CVAE with reconstructed flows emphasizes the critical role of input data preparation in boosting the fidelity of predictive models. The dual-layered error analysis in HF$^2$-VAD—utilizing both flow reconstruction and frame prediction errors—provides a robust framework that excels in anomaly discrimination.

In conclusion, HF$^2$-VAD emerges as a compelling advancement in the VAD field, predominantly due to its innovative framework marrying the strengths of flow reconstruction and future frame prediction. This work not only achieves notable practical improvements in anomaly detection accuracy but also sets a precedent for future exploration into hybrid anomaly detection models. The seamless integration of multiple data signals—such as optical flows and video frames—underlines the potential in utilizing comprehensive hybrid methodologies for complex vision problems. By further refining such integrated frameworks, future research may unlock even deeper insights and stronger performance in video understanding and analysis domains. As the field progresses, potential extensions could explore adaptive memory storage for evolving environments and integrating additional contextual cues to refine detection accuracies in diverse and dynamic scenes.