STEAD: Spatio-Temporal Efficient Anomaly Detection for Time and Compute Sensitive Applications

Abstract

This paper presents a new method for anomaly detection in automated systems with time and compute sensitive requirements with unparalleled efficiency. As systems like autonomous driving become increasingly popular, ensuring their safety has become more important than ever. With this motivation, this paper focuses on how to quickly and effectively detect various anomalies in the aforementioned systems. Many detection systems have been developed with great success under spatial contexts. However, there is still significant room for improvement when it comes to temporal context. While there is substantial work regarding this task, there is minimal work done regarding the efficiency of models and their ability to be applied to scenarios that require real-time inference. To address this gap, we propose STEAD (Spatio-Temporal Efficient Anomaly Detection), whose backbone is developed using (2+1)D Convolutions and Performer Linear Attention, which ensures computational efficiency without sacrificing performance. When evaluated on the UCF-Crime benchmark, our base model achieves an AUC of 91.34%, outperforming the previous SOTA (state of the art), and our fast version achieves an AUC of 88.87%, while having 99.70% less parameters and outperforming the previous SOTA as well. The code and pretrained models are made publicly available at https://github.com/agao8/STEAD.