Two novel motionbased algorithms for surveillance video analysis on embedded platforms This paper proposes two novel motionvector based techniques for target detection and target tracking in surveillance videos. The algorithms are designed to operate on a resourceconstrained device, such as a surveillance camera, and to reuse the motion vectors generated by the video encoder. The first novel algorithm for target detection uses motion vectors to construct a consistent motion mask, which is combined with a simple background segmentation technique to obtain a segmentation mask. The second proposed algorithm aims at multitarget tracking and uses motion vectors to assign blocks to targets employing five features. The weights of these features are adapted based on the interaction between targets. These algorithms are combined in one complete analysis application. The performance of this application for target detection has been evaluated for the iLIDS sterile zone dataset and achieves an F1score of 0.400.69. The performance of the analysis algorithm for multitarget tracking has been evaluated using the CAVIAR, dataset and achieves an MOTP of around 9.7 and MOTA of 0.170.25. On a selection of targets in videos from other datasets, the achieved MOTP and MOTA are 8.810.5 and 0.320.49 respectively. The execution time on a PCbased platform is 36 ms. This includes the 20 ms for generating motion vectors, which are also required by the video encoder.

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.