The aim of this project is to introduce a new smartphone-equipped Android application for assistance via the camera: in particular, the lane detection part will be covered. Using computer vision algorithms generated ad-hoc for the task; the application must be able to position, in the most practical way, people should maintain a straight line in any event and should stand within the line. This helps the administrator of the event to control the queue and can see that no fights occurs in event, this can be fueled by Deep Learning algorithms.
In this project, we describe how to control events and detect the intruders in the queues of the event. Lane tracks are provided in real time by the baseline video surveillance system. Given trajectory information, the event analysis module will attempt to determine whether or not a presence of intruder is currently being observed. However, due to real-time processing constrains, there might be false alarms generated by video image noise or non-human objects. It requires further intensive examination to filter out false event detections which can be processed in an off-line fashion.
We propose a system to detects the intruders in the queues by analyzing video surveillance. In low level task, a trajectory-based method processes trajectory data and detects intruder events in real time. In high level task, an intensive video analysis algorithm checks whether the detected intruder event is triggered or not.
Keywords: Coma Patient Tracker, Trace Movements, Activity Detection.
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