Anomaly Detection in Surveillance Video: An Unsupervised Unity Encoder Approach

Video anomaly detection in crowded environments is a critical task in surveillance systems to ensure public safety. Traditional approaches relying on handcrafted features, statistical models and even traditional deep learning models often struggle with the complexity of real-world scenarios. To address these challenges, this paper proposes a unique encoder-decoder based anomaly detection framework that incorporates skip connections to preserve spatial features during reconstruction and Tanh activation in the bottleneck for a balanced latent space. The approach uses a Reconstruction Error that combines Mean Squared Error (MSE) and Standard Deviation (SD) to improve sensitivity to fine anomalies. The proposed framework is evaluated on benchmark datasets i.e. UCSD Ped1, Ped2, Avenue and Shanghai. It achieved a superior performance with higher AUC and reduced Equal Error Rate (EER) thus demonstrating its effectiveness in detecting anomalies in complex environments.