# Innovative Approaches to Traffic Anomaly Detection and Classification Using AI
## 2 Algorithms and Methods
### Generative Adversarial Networks (GANs)  
Generative adversarial networks (GANs) have emerged as a promising approach for traffic anomaly detection, particularly in video surveillance contexts. By leveraging a competitive training process between a generator and a discriminator, GANs can effectively learn the distribution of normal traffic behavior. Once trained, they identify anomalies as instances the generator fails to reproduce accurately, which the discriminator then classifies as \"fake\". Compared to traditional machine learning and deep learning techniques such as CNNs or autoencoders, GANs offer a unique advantage in modeling complex data distributions without requiring labeled anomaly data. Several studies have demonstrated the effectiveness of GAN-based methods in detecting rare anomalies in traffic videos. These  
Figure 10: An illustration of the scenario where UCSD Ped2, ShanghaiTech, and CUHK Avenue are used as source datasets to learn a distance function from. Best viewed in color. From [39]. findings suggest that GANs can complement or outperform conventional approaches in certain scenarios, particularly when anomalies are diverse and sparsely represented. Leveraging the capabilities of GANs, the work presented in [40] introduces a novel method for video anomaly detection based on predicting future frames. This approach based on the idea that normal traffic events are predictable, while anomalies deviate from these expected patterns and are, therefore, less predictable. Unlike traditional methods that focus on reconstructing training data, this technique identifies anomalies by comparing predicted video frames with their actual counterparts. A U-Net architecture [41] predicts the next frame, using spatial constraints to minimize appearance differences and temporal constraints to align motion via optical flow. An adversarial training module enhances the realism of generated frames, as illustrated in Figure 11.  
Anomalies are detected using peak signal-to-noise ratio (PSNR) between predicted and actual frames, with normalized PSNR values serving as a regularity score for classification. The study is notable for introducing future frame prediction for anomaly detection, integrating motion constraints, and validating its approach on multiple public datasets (CUHK Avenue [38], UCSD Ped1 and Ped2 [37], and ShanghaiTech [33]), achieving superior area under the curve (AUC) results. Spatio-temporal adversarial networks (STANs) [42] is an advanced method for video anomaly detection that models \"normality\" in scenes by capturing spatio-temporal patterns. Its architecture consists of two key components: a bidirectional ConvLSTM-based generator, which creates intermediate frames from neighboring ones, and a 3D convolutional discriminator, which evaluates sequences for normality, as shown in Figure 12.  
STAN employs adversarial training, where the generator learns to produce realistic frames, and the discriminator adapts to differentiate real from generated sequences. An \"anomaly score\" is calculated during testing using generator loss (measuring discrepancies between generated and actual frames) and discriminator loss (assessing sequence authenticity). Higher scores indicate a higher likelihood of anomalies. Figure 11: Overview of the video frame prediction network pipeline. The model takes a sequence of frames as input and generates the predicted next frame using a U-Net-based generator. The prediction is supervised through multiple loss functions: intensity loss and gradient loss (between the predicted next frame and the real next frame), and optical flow loss computed by a pre-trained FlowNet model. FlowNet estimates the optical flow between both the predicted and the real frames, enforcing motion consistency. A discriminator is further used to differentiate between real and predicted frames, following an adversarial training scheme. From [40].