Automatic alert generation in a surveillance systems for smart city environment using deep learning algorithm

In Smart cities surveillance is an extremely important feature required for ensuring the safety of citizens and also for deterring the crime. Hence, intelligent video surveillance (IVS) frameworks are by and large increasingly more famous in security applications. The investigation and acknowledgment of anomalous practices in a video succession has step by step attracted the consideration in the field of IVS, as it permits sifting through an enormous number of pointless data, which ensures the high productivity in the security assurance, and spare a great deal of human and material assets. Techniques are proposed in the literature for analyzing the IVS systems. Existing systems for video analysis, suffer with some limitations. The one of the major limitation is lack of real time response from the surveillance systems. In order to overcome this limitation, an IVS system design is proposed using convolution neural networks. In case of emergency like fire, thieves’ attacks, Intrusion Detector, the proposed system sends an alert for the corresponding services automatically. Experimentation has done on the number of datasets available for video surveillance testing. The results show that the proposed surveillance system achieves very low false alarm rates.