Closed circuit television systems (CCTV) play a vital role in evidence collection against crimes and criminals. The existing systems does not classify normal and abnormal events leading the police to become more reluctant to attend the crime scenes unless there was a visual verification, either by manned patrols or by electronic images from the surveillance cameras. The Proposed work is being used for surveillance, monitoring and classifications of weapons, live tracking and many more purposes. In this work, live surveillance videos is taken for monitoring and detecting the abnormal events based on real time image processing techniques. Operations of proposed project has three processing modules, first processing module is for object detection using Convolutional Neural Networks(CNN) and second processing module will handle the classification of weapons, monitoring and alarm operations will be carried out by the third processing module. CCTV will monitor circular area and it will automatically perform all operations and be controlled. Shape detection algorithms and object detection algorithms have been tested to find accuracy in detection and analysis the processing time before implementing in such environment and results provide optimal accuracy in matching weapons and objects type with name and shape in predefined database like ALEXNET. The proposed work drastically reduces the crime rate and it also provide a higher level security in certain areas and it will reduce the time required to catch the criminal.
Video surveillance, abnormal events, object detection, CNN, ALEXNET
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