People Tracking and Counting using Surveillance Systems

Now a days Tracking individual person using a CNN (Convolutional Neural Network) for computer vision of AI ( Artificial Intelligence) is an active era of research. Pedestrian tracking will use different things like Color, Texture , and Shape’s to identify Individual subjects in a Video clip and track the Person until that person is out of that particular scene. Things like this makes our life easy in counting Pedestrian’s in an rush or crowdie area, also helps in identifying the most important things in an event area. In this journal user will explore How user can up-skill our DCNN ( Deep Convolutional Neural Network) which will help us in successful tracking and counting using the both Yolo and OpenCV. The most crucial situation while performing monitoring individuals from an video static scene is able to track the pedestrian’s under an occlusion conditions .Here ,user will develop an strategy which will track and count pedestrians automatically in an surveillance system. The methodology whichuser are using for tracking is Centroid Tracker , It will helps us in situations like overlap tracker and Merging splitting scenario.