Author:

Keywords:

Science & Technology, Technology, Computer Science, Artificial Intelligence, Computer Science, Hardware & Architecture, Imaging Science & Photographic Technology, Computer Science

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.