Detection of Suspicious Pedestrian Behaviour Using Modified Probabilistic Neural Networks

In large scale visual surveillance applications, classification of human behaviors is very important. Classes of interest include suspicious human behaviors which should be effectively detected so as to alert supervisors' attention. In this paper, a data-based neural network such as the Modified Probabilistic Neural Network (MPNN) is introduced to approximately partition the classification space nonlinearly in order to achieve an acceptable classification performance while reducing computational complexity. The paper shows that this kind of network is able to achieve a good trade-off between classification accuracy and computational complexity. The performance of MPNN is compared to that of more conventional classification methods such as Hidden Markov Models (HMM) and the Multilayer Perceptron (MLP).

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