Tracking the invisible man: hidden-object detection for complex visual scene understanding

Reliable detection of objects of interest in complex visual scenes is of prime importance for video-surveillance applications. While most vision approaches deal with tracking visible or partially visible objects in single or multiple video streams, we propose a new approach to automatically detect all objects of interest being part of an analyzed scene, even those entirely hidden in a camera view whereas being present in the scene. For that, we have developed an innovative artificial-intelligence framework embedding a computer vision process fully integrating symbolic knowledge-based reasoning. Our system has been evaluated on standard datasets consisting of video streams with real-world objects evolving in cluttered, outdoor environment under difficult lighting conditions. Our proposed approach shows excellent performance both in detection accuracy and robustness, and outperforms state-of-the-art methods.