Neural network classifiers for automated video surveillance

Tony Jan, Massimo Piccardi, Thomas Hintz

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)


In automated visual surveillance applications, detection of suspicious human behaviors is of great practical importance. However due to random nature of human movements, reliable classification of suspicious human movements can be very difficult. Artificial Neural Network (ANN) classifiers can perform well however their computational requirements can be very large for real time implementation. In this paper, a data-based modeling neural network such as Modified Probabilistic Neural Network (MPNN) is introduced which partitions the decision space nonlinearly in order to achieve reliable classification, however still with acceptable computations. The experiment shows that the compact MPNN attains good classification performance compared to t h a t of other larger conventional neural network based classifiers such as Multilayer Perceptron (MLP) and Self Organising Map (SOM).

Original languageEnglish
Title of host publication2003 IEEE 13th Workshop on Neural Networks for Signal Processing, NNSP 2003
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages10
ISBN (Electronic)0780381777
Publication statusPublished - 2003
Externally publishedYes
Event13th IEEE Workshop on Neural Networks for Signal Processing, NNSP 2003 - Toulouse, France
Duration: 17 Sept 200319 Sept 2003

Publication series

NameNeural Networks for Signal Processing - Proceedings of the IEEE Workshop


Conference13th IEEE Workshop on Neural Networks for Signal Processing, NNSP 2003


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