Abnormal event detection model using an improved ResNet101 in context aware surveillance system

Abstract

Surveillance system plays a significant role for achieving security monitoring in the place of crowd areas. Offline monitoring of these crowd activity is quite challenging because it requires huge number of human resources for attaining efficient tracking. For shortcoming these issue automated and intelligent based system must be developed for efficiently monitor crowd and detect abnormal activity. However the existing methods faces issues like irrelevant features, high cost and process complexity. In this current research context aware surveillance-system utilising hybrid ResNet101-ANN is developed for effective abnormal activity detection. For this proposed approach video acquired from surveillance camera is considered as input. Then, acquired video is segmented into multiple frames. After that pre-processing techniques such as denoising using mean filter, motion deblurring, contrast enhancement using Histogram Equalisation and canny edge detection is applied in this segmented frames. Further, the pre-processed frame is fetched into hybrid ResNet101-ANN classifier for abnormal event classification. Here, ResNet101 is used for extracting the features from the frames and Artificial neural network which replaces the fully connected layer of ResNet101 us used to detect the abnormal activity. If once abnormal-events detected the context aware services generate alert to the user for preventing abnormal-activities. Accuracy, precision, recall, and error values reached for the proposed-model on simulation were 0.98, 0.98, 0.98 and 0.017 respectively. Using this proposed model effective crowd monitoring and abnormal activity detection can be achieved.