FlexAE: A Self-Conditioned Detector To Prevent Model Overfitting For Unsupervised Video Anomaly Detection

Unsupervised Video Anomaly Detection (VAD) has garnered significant attention for its ability to exploit unlabeled videos. However, VAD faces two primary challenges arising from the absence of labels: (i) Striking a balance between overfitting and underfitting, and (ii) Optimal parameter tuning. To tackle these challenges, we propose a novel detector named Flexible AutoEncoder (FlexAE). A fitting-parameter is introduced to regulate the model’s fitting capacity, and a novel Negative Learning (NL) mechanism is integrated to mitigate the influence of anomalies during training. For self-conditioning, a novel algorithm is devised to autonomously update the fitting-parameter and the threshold used in NL based on the reconstruction error. Comprehensive experiments on two benchmark datasets, UCF-Crime and ShanghaiTech, demonstrate that our proposed FlexAE outperforms state-of-the-art methods without the need for manual hyperparameter tuning.