Abnormal behavior detection in LWIR surveillance of railway platforms

Abstract

In this paper we present a framework that is able to reliably and completely autonomously detect abnormal behavior in surveillance images. As input, we rely solely on a long-wave infrared (LWIR) image sensor. Our abnormal behavior detection pipeline consists of two consecutive stages. In a first stage, we perform efficient and fast pedestrian detection and tracking. In a second step, the detected paths are fed into a semi-supervised classifier that detects abnormal behavior. As test-case we recorded a unique real-life LWIR train station dataset -- which will be made publicly available -- containing natural occurrences of both normal and abnormal behavior. Our experiments indicate that our proposed framework achieves excellent accuracy results at real-time processing speeds. ispartof: Proceedings of the 14th IEEE International Conference on Advanced Video and Signal based Surveillance (AVSS) ispartof: The IEEE International Conference on Advanced Video and Signal based Surveillance (AVSS) location:Lecce, Italy date:29 Aug - 1 Sep 2017 status: published