Video Anomaly Detection and Localization via Gaussian Mixture Fully Convolutional Variational Autoencoder (1805.11223v1)

Abstract: We present a novel end-to-end partially supervised deep learning approach for video anomaly detection and localization using only normal samples. The insight that motivates this study is that the normal samples can be associated with at least one Gaussian component of a Gaussian Mixture Model (GMM), while anomalies either do not belong to any Gaussian component. The method is based on Gaussian Mixture Variational Autoencoder, which can learn feature representations of the normal samples as a Gaussian Mixture Model trained using deep learning. A Fully Convolutional Network (FCN) that does not contain a fully-connected layer is employed for the encoder-decoder structure to preserve relative spatial coordinates between the input image and the output feature map. Based on the joint probabilities of each of the Gaussian mixture components, we introduce a sample energy based method to score the anomaly of image test patches. A two-stream network framework is employed to combine the appearance and motion anomalies, using RGB frames for the former and dynamic flow images, for the latter. We test our approach on two popular benchmarks (UCSD Dataset and Avenue Dataset). The experimental results verify the superiority of our method compared to the state of the arts.

• The paper introduces a GMFC-VAE that models latent normal behavior as distinct Gaussian components to accurately detect video anomalies.
• It employs a two-stream fully convolutional network architecture that integrates RGB and dynamic flow cues to capture both spatial and temporal anomalies.
• Empirical results achieve state-of-the-art performance with frame-level AUC scores up to 94.9% on datasets like UCSD Ped1, underscoring its robustness and efficiency.

Video Anomaly Detection and Localization via Gaussian Mixture Fully Convolutional Variational Autoencoder

The paper "Video Anomaly Detection and Localization via Gaussian Mixture Fully Convolutional Variational Autoencoder" by Fan et al. proposes an advanced deep learning framework aimed at addressing the challenges of anomaly detection and localization in video surveillance scenarios. This research presents a partially supervised methodology exploiting only normal samples to identify anomalies, which are defined as instances that cannot be associated with the learned representation of normal behavior. The cornerstone of this approach is the Gaussian Mixture Variational Autoencoder (GMFC-VAE), combined with a two-stream fully convolutional neural network architecture to capture both appearance and motion anomalies efficiently.

The work introduces several notable contributions that mark significant progress in video anomaly detection. The framework is distinguished by its capacity to model the latent representation of normal samples as a Gaussian Mixture Model (GMM), leveraging the deep learning capabilities of the Variational Autoencoder (VAE). This provides a probabilistic clustering mechanism where normal behaviors correspond to specific Gaussian components, and any deviation from this cluster is flagged as an anomaly. The core innovation lies in the ability to detect both spatial (appearance) and temporal (motion) anomalies through separate yet complementary streams using RGB frames and dynamic flow images.

Dynamic flow, in contrast to traditional optical flow, encapsulates broader motion cues over multiple frames, thus offering enhanced action characterization pertinent to real-time surveillance applications. The use of fully convolutional networks (FCNs) in the encoder-decoder setup ensures the preservation of spatial details while significantly mitigating the loss of information that typically occurs in pooling operations found in conventional convolutional neural networks.

Empirically, the paper's methodology outperforms established state-of-the-art methods across several metrics in notable datasets such as UCSD Ped1, Ped2, and Avenue. In terms of quantitative results, frame-level AUC scores reached as high as 94.9% on the UCSD Ped1, showcasing substantial robustness and reliability in anomaly detection scenarios. The use of a sample energy-based method for scoring anomaly likelihood boosts detection precision, making it a competitive choice for operational deployment in video surveillance systems.

Beyond the technical advancements, the paper provides a detailed account of the neural architecture and the novel training scheme employed, including a comprehensive evaluation on a mixture of Gaussian components—highlighting practical considerations such as computational cost versus performance gain. This method's partial supervision aspect reduces the burden of data labeling, a pivotal advantage in large-volume surveillance streams where anomalous events are sparsely distributed.

The implications of this research are multifaceted, enhancing automatic surveillance systems by integrating sophisticated deep learning techniques that reduce human intervention in anomaly detection. Future potential for this work includes extending the framework to more complex scenes and incorporating early fusion of appearance and motion cues to streamline the neural architecture while maintaining high anomaly detection accuracy. Moreover, the extension to other domains such as traffic flow analysis, robotics, and behavioral monitoring could further solidify its applicability across various real-world scenarios. The advancements presented in this paper suggest a promising trajectory for anomaly detection methodologies in the expanding field of intelligent video surveillance.