Smart Video Survillance Based Weapon Identification Using Yolov5

Video Surveillance plays an important role in every aspect of life like theft detection, unusual happenings in crowded places, monitoring the suspicious activities of each individual to provide a secure and hassle free environment. Footage of closed circuit television (CCTV) camera is taken as an evidence to track the suspicious act. It is very tough to operate surveillance cameras with human intervention to detect abnormal activities. Fully automating surveillance with smart video capturing capabilities using deep learning technique is one of the most advanced means of remotely monitoring strange activities with exact location, time of event occurred along with facial recognition of criminal. Finding misdemeanor activity in a public place is very difficult to observe, as many objects are involved in the real time scenario. An uncommon or doubtful incidents in public places are captured in CCTV cameras which promotes police force to safeguard people before any mishap happens. It helps police to reach that spot on time and rescue victim. All these are meant to be achieved by using YOLO (You Only Look Once) object detection models and its variants like YOLO V1, V2, V3, V4 and latest V5 which is 88% faster than yolov4 in Deep Learning. This proposed system helps in identifying weapons held by a person as well as face recognition to identify the suspicious user. Using YOLO v5, it is very simple to track objects like weapons in a crowd. Low resolution images, far away and out of focus in the scene can also be captured and identified accurately