Visible to the public A deep learning approach to trespassing detection using video surveillance data

TitleA deep learning approach to trespassing detection using video surveillance data
Publication TypeConference Paper
Year of Publication2019
AuthorsBashir, Muzammil, Rundensteiner, Elke A., Ahsan, Ramoza
Conference Name2019 IEEE International Conference on Big Data (Big Data)
Keywordsactivity detection, automated railroad trespassing detection system, background subtraction, CNN-based deep learning architecture, Computer architecture, Computer vision, convolutional neural nets, deep convolutional neural networks, Deep Learning, Deep Neural Network, deep video, Detectors, dual-stage ARTS architecture, dual-stage deep learning architecture, early warning prediction techniques, high fidelity trespass classification stage, inexpensive pre-filtering stage, learning (artificial intelligence), machine learning, Metrics, Neural networks, potential trespassing sites, pubcrawl, public domain surveillance data, Railroad security, railroad trespassing activity, resilience, Resiliency, safety risks, Scalability, Subspace constraints, surveillance, Trespassing detection, video processing, video surveillance, video surveillance data
AbstractRailroad trespassing is a dangerous activity with significant security and safety risks. However, regular patrolling of potential trespassing sites is infeasible due to exceedingly high resource demands and personnel costs. This raises the need to design automated trespass detection and early warning prediction techniques leveraging state-of-the-art machine learning. To meet this need, we propose a novel framework for Automated Railroad Trespassing detection System using video surveillance data called ARTS. As the core of our solution, we adopt a CNN-based deep learning architecture capable of video processing. However, these deep learning-based methods, while effective, are known to be computationally expensive and time consuming, especially when applied to a large volume of surveillance data. Leveraging the sparsity of railroad trespassing activity, ARTS corresponds to a dual-stage deep learning architecture composed of an inexpensive pre-filtering stage for activity detection, followed by a high fidelity trespass classification stage employing deep neural network. The resulting dual-stage ARTS architecture represents a flexible solution capable of trading-off accuracy with computational time. We demonstrate the efficacy of our approach on public domain surveillance data achieving 0.87 f1 score while keeping up with the enormous video volume, achieving a practical time and accuracy trade-off.
Citation Keybashir_deep_2019