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Abstract
Automated visual surveillance is one of the most actively researched areas in the past decade. Although current behaviour recognition systems provide us with a good understanding on the behaviour of individual moving objects present in an observed scene, they are not able to efficiently recognize the behaviour of groups formed by large numbers of moving objects. In this thesis, we present a HMM-based group behaviour recognition system which is capable of recognizing group behaviours effectively and efficiently. In our approach, we generate synthetic data for the training and validation of our behaviour recognition system. In addition, we use a single feature vector to represent the group dynamics, instead of using one feature vector for each pairwise interaction. Experimental results show accurate classification for both real-life data and simulated data from Lee's dataset [1]. Therefore, we conclude that the proposed approach is a viable and accurate technique to perform group behaviour recognition in both simulated environment and real-life situations. Moreover, the high accuracy of the classification results obtained on real-life data, when only synthetic data was used for the training, suggests that it is possible to develop group behaviour models using synthetic data alone.
