Occlusion and split detection and correction for object tracking in surveillance applications This paper proposes a novel algorithm for the realtime detection and correction of occlusion and split in featurebased tracking of objects for surveillance applications. The proposed algorithm detects sudden variations of spatiotemporal features of objects in order to identify possible occlusion or split events. The detection is followed by a validation stage that uses past tracking information to prevent false detection of occlusion or split. Special care is taken in case of heavy occlusion, when there is a large superposition of objects. In this case the system relies on longterm temporal behavior of objects to avoid updating the video object features with unreliable (e.g. shape and motion) information. Occlusion is corrected by separating occluded objects. For the detection of splits, in addition to the analysis of spatiotemporal changes in objects features, our algorithm analyzes the temporal behavior of split objects to discriminate between errors in segmentation and real separation of objects, such as in the deposit of an object. Split is corrected by physically merging the objects detected to be split. To validate the proposed approach, objective and visual results are presented. Experimental results show the ability of the proposed algorithm to detect and correct, both, split and occlusion of objects. The proposed algorithm is most suitable in video surveillance applications due to: its good performance in multiple, heavy, and total occlusion; its distinction between real object separation and faulty object split; its handling of simultaneous occlusion and split events; and its low computational complexity.

OnLine Visual Tracking with Occlusion Handling One of the core challenges in visual multitarget tracking is occlusion. This is especially important in applications such as video surveillance and sports analytics. While offline batch processing algorithms can utilise future measurements to handle occlusion effectively, online algorithms have to rely on current and past measurements only. As such, it is markedly more challenging to handle occlusion in online applications. To address this problem, we propagate information over time in a way that it generates a sense of deja vu when similar visual and motion features are observed. To achieve this, we extend the Generalized Labeled MultiBernoulli (GLMB) filter, originally designed for tracking pointsized targets, to be used in visual multitarget tracking. The proposed algorithm includes a novel false alarm detectionremoval and label recovery methods capable of reliably recovering tracks that are even lost for a substantial period of time. We compare the performance of the proposed method with the stateoftheart methods in challenging datasets using standard visual tracking metrics. Our comparisons show that the proposed method performs favourably compared to the stateoftheart methods, particularly in terms of ID switches and fragmentation metrics which signifies occlusion.