Foreground Object Segmentation and Shadow Detection for Video Sequences in Uncontrolled Environments
Place: Large Lecture Room, Computer Vision Center
Affiliation: Computer Vision Center / Univ. Autonoma de Barcelona, Spain
This Thesis is mainly divided in two parts. The first one presents a study of motion segmentation problems. Based on this study, a novel algorithm for mobile-object segmentation from a static background scene is also presented. This approach is demonstrated robust and accurate under most of the common problems in motion segmentation. The second one tackles the problem of shadows in depth. Firstly, a bottom-up approach based on a chromatic shadow detector is presented to deal with umbra shadows. Secondly, a top-down approach based on a tracking system has been developed in order to enhance the chromatic shadow detection.
In our first contribution, a case analysis of motion segmentation problems is presented by taking into account the problems associated with different cues, namely colour, edge and intensity. Our second contribution is a hybrid architecture which handles the main problems in the case analysis, by fusing (i) these three cues and (ii) a temporal difference algorithm. On the one hand, we enhance the colour and edge models to solve both global/local illumination changes (shadows and highlights) and camouflage in intensity. In addition, local information is exploited to cope with camouflage in chroma. On the other hand, the intensity cue is also applied when colour and edge cues are not available, such as when beyond the dynamic range. Additionally, temporal difference is included to segment motion when these three cues are not available. Lastly, the approach is enhanced for allowing ghost detection.
Most segmentation approaches dealing with shadow detection are typically restricted to penumbra shadows. Therefore, such techniques cannot cope well with umbra shadows. In the Bottom-up part, the shadow detection approach applies a novel technique based on gradient and colour models for separating chromatic moving shadows from moving objects. Hereafter, the regions corresponding to potential shadows are grouped by considering ''a bluish effect'' and an edge partitioning. Lastly, (i) temporal similarities between local gradient structures and (ii) spatial similarities between chrominance angle and brightness distortions are analysed for all potential shadow regions in order to finally identify umbra shadows. In the top-down process, after detection of objects and shadows both are tracked using Kalman filters. Firstly, this implies a data association and a case analysis between the blobs (foreground and shadow) and Kalman filters. Based on this association, temporal consistency is looked for the association between foregrounds and shadows and their respective Kalman Filters. From this association several cases are studied, as a result lost chromatic shadows are correctly detected.
As a result, our approach obtains very accurate and robust motion segmentation in both indoor and outdoor scenarios, as quantitatively and qualitatively demonstrated in the experimental results, by comparing our approach with most best-known state-of-the-art approaches.