Video analytics is a technology that processes a digital video signal using a special algorithm to perform a security- related function [12]. There is a need to design an automated trespass detection and early warning prediction tool leveraging state-of-the-art machine learning techniques. Leveraging video surveillance through security cameras [3]. In particular, they adopt a CNN-based deep learning architecture (Faster-RCNN) as the core component of solution. However, these deep learningbased methods, while effective, are known to be computationally expensive and time consuming, especially when applied to a large amount of surveillance data [3]. Given the sparsity of railroad trespassing activity, design a dual-stage deep learning architecture composed of an inexpensive prefiltering stage for activity detection followed by a high fidelity trespass detection stage for robust classification. The former is responsible for filtering out frames that show little to no activity, this way reducing the amount of data to be processed by the later more compute-intensive stage which adopts state-of-the-art Faster- RCNN to ensure effective classification of trespassing activity [3]. no vehicle entry zone, no parking zone, smart home security [14], etc.

Keywords : Intruder Tracking, Trespass Detection, Video Analytics, Security