Intelligent Crime Anomaly Detection in Smart Cities Using Deep Learning

The quick and accurate identification of criminal activity is paramount to securing any residence. With the rapid growth of smart cities, the integration of crime detection systems seeks to improve this security. In the past a strong reliance has been put on standard video surveillance in order to achieve this goal. This often creates a backlog of video data that must be monitored by a supervising official. For large urban areas, this creates a increasingly large workload for supervising officials which leads to an increase in error rate. Solutions have been implemented to help reduce the workload. Currently, auto regressive models have been used to better forecast criminal acts, but also have a list of shortcomings. We propose a solution of using neural networks in combination with a Hybrid Deep Learning algorithm to analyze video stream data. Our system will be able to quickly identify and assess criminal activity which will in turn reduce workloads on the supervising officials. When implemented across smart city infrastructure it will allow for a efficient and adaptable crime detection system.