Topological tracking of connected components in image sequences

Persistent homology provides information about the lifetime of homology classes along a filtration of cell complexes. Persistence barcode is a graphi- cal representation of such information. A filtration might be determined by time in a set of spatiotemporal data, but classical methods for computing persistent homology do not respect the fact that we can not move back- wards in time. In this paper, taking as input a time-varying sequence of two-dimensional (2D) binary digital images, we develop an algorithm for en- coding, in the so-called spatiotemporal barcode, lifetime of connected compo- nents (of either the foreground or background) that are moving in the image sequence over time (this information may not coincide with the one provided by the persistence barcode). This way, given a connected component at a specific time in the sequence, we can track the component backwards in time until the moment it was born, by what we call a spatiotemporal path. The main contribution of this paper with respect to our previous works lies in a new algorithm that computes spatiotemporal paths directly, valid for both foreground and background and developed in a general context, setting the ground for a future extension for tracking higher dimensional topological features in nD binary digital image sequences.Ministerio de Economía y Competitividad MTM2015-67072-

A Neural System for Automated CCTV Surveillance

This paper overviews a new system, the “Owens Tracker,” for automated identification of suspicious pedestrian activity in a car-park. Centralized CCTV systems relay multiple video streams to a central point for monitoring by an operator. The operator receives a continuous stream of information, mostly related to normal activity, making it difficult to maintain concentration at a sufficiently high level. While it is difficult to place quantitative boundaries on the number of scenes and time period over which effective monitoring can be performed, Wallace and Diffley [1] give some guidance, based on empirical and anecdotal evidence, suggesting that the number of cameras monitored by an operator be no greater than 16, and that the period of effective monitoring may be as low as 30 minutes before recuperation is required. An intelligent video surveillance system should therefore act as a filter, censuring inactive scenes and scenes showing normal activity. By presenting the operator only with unusual activity his/her attention is effectively focussed, and the ratio of cameras to operators can be increased. The Owens Tracker learns to recognize environmentspecific normal behaviour, and refers sequences of unusual behaviour for operator attention. The system was developed using standard low-resolution CCTV cameras operating in the car-parks of Doxford Park Industrial Estate (Sunderland, Tyne and Wear), and targets unusual pedestrian behaviour. The modus operandi of the system is to highlight excursions from a learned model of normal behaviour in the monitored scene. The system tracks objects and extracts their centroids; behaviour is defined as the trajectory traced by an object centroid; normality as the trajectories typically encountered in the scene. The essential stages in the system are: segmentation of objects of interest; disambiguation and tracking of multiple contacts, including the handling of occlusion and noise, and successful tracking of objects that “merge” during motion; identification of unusual trajectories. These three stages are discussed in more detail in the following sections, and the system performance is then evaluated

Spatiotemporal Barcodes for Image Sequence Analysis

Taking as input a time-varying sequence of two-dimensional (2D) binary images, we develop an algorithm for computing a spatiotemporal 0–barcode encoding lifetime of connected components on the image sequence over time. This information may not coincide with the one provided by the 0–barcode encoding the 0–persistent homology, since the latter does not respect the principle that it is not possible to move backwards in time. A cell complex K is computed from the given sequence, being the cells of K classified as spatial or temporal depending on whether they connect two consecutive frames or not. A spatiotemporal path is defined as a sequence of edges of K forming a path such that two edges of the path cannot connect the same two consecutive frames. In our algorithm, for each vertex v ∈ K, a spatiotemporal path from v to the “oldest” spatiotemporally-connected vertex is computed and the corresponding spatiotemporal 0–bar is added to the spatiotemporal 0–barcode.Junta de Andalucía FQM-369Ministerio de Economía y Competitividad MTM2012-3270

Application of the self-organising map to trajectory classification

This paper presents an approach to the problem of automatically classifying events detected by video surveillance systems; specifically, of detecting unusual or suspicious movements. Approaches to this problem typically involve building complex 3D-models in real-world coordinates to provide trajectory information for the classifier. In this paper we show that analysis of trajectories may be carried out in a model-free fashion, using self-organising feature map neural networks to learn the characteristics of normal trajectories, and to detect novel ones. Trajectories are represented using positional and first and second order motion information, with moving-average smoothing. This allows novelty detection to be applied on a point-by-point basis in real time, and permits both instantaneous motion and whole trajectory motion to be subjected to novelty detection