The bottleneck in situation awareness is no longer in the sensing domain but rather in the data interpretation domain, since the number of sensors is rapidly increasing and it is not affordable to increase human data-analysis capacity at the same rate. Automatic image analysis can assist a human analyst by alerting when an event of interest occurs. However, common state-of-the-art image recognition systems learn representations in high-dimensional feature spaces, which makes them less suitable to generate a user-comprehensive message. Such data-driven approaches rely on large amounts of training data, which is often not available for quite rare but high-impact incidents in the security domain. The key contribution of this paper is that we present a novel real-time system for image understanding based on generic instantaneous low-level processing components (symbols) and flexible user-definable and user-understandable combinations of these components (sentences) at a higher level for the recognition of specific relevant events in the security domain. We show that the detection of an event of interest can be enhanced by utilizing recognition of multiple short-term preparatory actions.
Klamer Schutte, Gertjan Burghouts, Nanda van der Stap, Victor Westerwoudt, Henri Bouma, Maarten Kruithof, Jan Baan, and Johan-Martijn ten Hove, "Long-term behavior understanding based on the expert-based combination of short-term observations in high-resolution CCTV," Proc. SPIE 9995, Optics and Photonics for Counterterrorism, Crime Fighting, and Defence XII, 99950Q (Presented at SPIE Security + Defence: September 27, 2016; Published: 24 October 2016); https://doi.org/10.1117/12.2241180.
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