Detection of Anomalous Behavioural Patterns In University Environment Using CNN-LSTM
Dorcas Oladayo Esan, Pius Adewale Owolawi, Chuling Tu · 2020
The increase in the crime rate has led to the deployment of surveillance systems to different places such as airports, malls and schools in order to prevent crime. However, inaccurate detection of anomalous behavioural patterns due to false detection (false alarm) errors is the main challenge that affects the performance of surveillance systems. Although several interesting techniques have been reported in literature for the detection of anomalous behaviour using semi-supervised and supervised techniques, these still require improvement in terms of accuracy and minimizing of false detection errors in crowded environments. This paper presents a performance analysis of the convolutional neural network with long short-term memory (CNN-LSTM) in surveillance systems. The image features are extracted from the image frame sequences using the CNN, while the LSTM uses the gate mechanism to keep vital information for remembrance. The results are compared with existing detection models, including the mixture of probabilistic principal analysis, motion deep net, social force and dictionary-based models. Experiments are done on the University of California San Diego dataset using the proposed anomalous behavioural pattern detection system. The results obtained show that the proposed system outperforms the others mentioned with 86% accuracy.