VideoZoom: Summarizing surveillance images for safeguards video reviews

This report presents VideoZoom, a prototype review tool that builds automatic summaries out of sequences of surveillance images taken by cameras with a fixed point of view. These summary images are then visualised in a zooming user interface allowing the discovery and annotation of images of interest. The prototype system was used for detection of safeguards-relevant events in image sequences acquired in nuclear facilities. A first evaluation of the prototype system with inspectors from DG-ENER was performed. Results indicate that the system allows accurate reviews, can save effort and is easy to learn and use. In addition the system allows detection of unexpected events which would be missed by standard review tools.JRC.E.8-Nuclear securit

Detection and Classification of Multiple Person Interaction

Institute of Perception, Action and BehaviourThis thesis investigates the classification of the behaviour of multiple persons when viewed from a video camera. Work upon a constrained case of multiple person interaction in the form of team games is investigated. A comparison between attempting to model individual features using a (hierarchical dynamic model) and modelling the team as a whole (using a support vector machine) is given. It is shown that for team games such as handball it is preferable to model the whole team. In such instances correct classification performance of over 80% are attained. A more general case of interaction is then considered. Classification of interacting people in a surveillance situation over several datasets is then investigated. We introduce a new feature set and compare several methods with the previous best published method (Oliver 2000) and demonstrate an improvement in performance. Classification rates of over 95% on real video data sequences are demonstrated. An investigation into how the length of time a sequence is observed is then performed. This results in an improved classifier (of over 2%) which uses a class dependent window size. The question of detecting pre/post and actual fighting situations is then addressed. A hierarchical AdaBoost classifier is used to demonstrate the ability to classify such situations. It is demonstrated that such an approach can classify 91% of fighting situations correctly

Hidden Markov Models for Optical Flow Analysis in Crowds

This paper is a postprint of a paper submitted to and accepted for publication in ICPR 2006 and is subject to IEEE copyright. This paper presents an event detector for emergencies in crowds. Assuming a single camera and a dense crowd we rely on optical flow instead of tracking statistics as a feature to extract information from the crowd video data. The optical flow features are encoded with Hidden Markov Models to allow for the detection of emergency or abnormal events in the crowd. In order to increase the detection sensitivity a local modelling approach is used. The results with simulated crowds show the effectiveness of the proposed approach on detecting abnormalities in dense crowds.