Abnormal Event Detection in Videos using Generative Adversarial Nets (1708.09644v1)

Abstract: In this paper we address the abnormality detection problem in crowded scenes. We propose to use Generative Adversarial Nets (GANs), which are trained using normal frames and corresponding optical-flow images in order to learn an internal representation of the scene normality. Since our GANs are trained with only normal data, they are not able to generate abnormal events. At testing time the real data are compared with both the appearance and the motion representations reconstructed by our GANs and abnormal areas are detected by computing local differences. Experimental results on challenging abnormality detection datasets show the superiority of the proposed method compared to the state of the art in both frame-level and pixel-level abnormality detection tasks.

• The paper introduces a GAN-based framework that models normal behavior to detect deviations in crowded video scenes.
• It utilizes bidirectional mapping between video frames and optical-flow images to accurately highlight abnormal patterns.
• Evaluation on benchmark datasets shows significant improvements, achieving high AUC scores at both frame- and pixel-level detection.

Overview of "Abnormal Event Detection in Videos using Generative Adversarial Nets"

This paper presents a novel method for detecting abnormal events in crowded scenes by leveraging Generative Adversarial Networks (GANs). The authors, Ravanbakhsh et al., propose a generative approach that models normal patterns of crowd behavior using only normal data during the training phase. This technique inherently focuses on identifying deviations from the learned norm, which are indicative of potential abnormalities.

Methodology

The core idea is to train GANs to represent normal crowd activities using normal video frames and their corresponding optical-flow images. By training two networks—one to generate optical-flow images from video frames and the other to generate video frames from optical flow—the system effectively learns a bidirectional mapping of normal behavior. During testing, any failure to accurately reconstruct these mappings when fed with new data suggests the presence of abnormal events.

1. Generative Adversarial Networks: The GAN architectures utilize a generator-discriminator pair, where the generator attempts to create realistic representations from input data, while the discriminator assesses their authenticity. The conditional GAN framework is adopted, whereby both generators are conditioned on real input data to synthesize accurate optical-flow and frame representations.
2. Detection Strategy: At the detection stage, differences between actual video characteristics and GAN-generated characteristics (both in appearance and motion) are calculated. Substantial discrepancies highlight abnormal patterns. The paper leverages both pixel-level and semantic-level differences to create a comprehensive abnormality map.

Results

The authors conducted extensive evaluations on benchmark datasets, namely UCSD Pedestrian (Ped1 and Ped2 subsets) and UMN SocialForce, demonstrating that their method surpasses existing state-of-the-art techniques in abnormality detection. Quantitatively, their approach achieved an Area Under Curve (AUC) of 97.4% for frame-level detection on UCSD Ped1, and 70.3% at the pixel-level, showing significant improvements in both detection accuracy and robustness. Moreover, the method provides promising results in diverse conditions and complex crowd environments.

Implications and Future Directions

This work has significant implications for video-surveillance applications, particularly in enhancing public safety by automating the detection of anomalies in crowded areas with minimal supervision. By eliminating the need for explicitly labeled abnormal event data in the training process, the approach promises scalability and adaptability across different settings and scenarios.

For future research, the authors suggest exploring the integration of dynamic images to represent motion over multiple frames as a potential enhancement to their method. Such advancements could further improve the accuracy and reliability of abnormal event detection in real-world applications.

In conclusion, the adoption of GANs in modeling crowd behavior represents a substantive step forward in video analysis for abnormality detection. The proposed approach effectively bridges the gap between theoretical innovation in generative modeling and practical utility in surveillance contexts.