Deep Learning Approach for Suspicious Activity Detection from Surveillance Video

Abstract

Abstract: In today's uncertain world, video surveillance plays a vital role in maintaining indoor and outdoor security. Video surveillance system components such as behaviour detection, behaviour understanding, and normal or suspicious activity classification can be used for real-time applications. This article uses a hierarchical approach to detect various suspicious activities such as loitering, fainting, and trespassing. This approach is based on motion properties between different objects. First, various suspicious activities are defined using a semantic approach. Object detection is then performed by background subtraction. Detected objects are classified as live (human) or non-live (bag). These objects need to be tracked and this is done using correlation techniques. Finally, motion features and temporal information are used to classify events as normal or suspicious. A semantics-based approach is used, resulting in low computational complexity and high efficiency of the approach.