Real-Time Human Detection in Video Surveillance - Sravanth, Chalavadi; Harshavardhan, Gadde; Kavya, Kamineni; Akbar, Shaik Mohammad; Baba, Ch.M.H. Sai (International Journal of Computer Sciences and Engineering, 2021)
The basic Fundamental to human-centric computer vision is to make the human motion see and understandable by machines. The hectic task is that the video containing enormous amount of information in the form of pixels, much of meaningless to a computer unless it can decode the data within the pixels. To make it possible, computer what is the mechanism behind which pixel go together and what it represents. The process of detecting and tracking the pixels representing the form of humans is to be notified as Human motion capture. Where there is a lacking of count of the people and we want to overcome. We plan to achieve this goal using intermediate level deep learning project on computer vision concepts, where deep learning is an AI method that imitate the functioning of human brain in processing data for use of object detection, speech recognition, translating languages, and making decisions. OpenCV is the place where it deals will all sorts of camera related things and make the detection easier. This work represents that how a human is detected and counted using SVM. The main idea is to detect the patterns of human motion, to a larger extent which is independent of differences in appearance. To do so, an HOG descriptor is used to detect the patterns of the frame captured, the greatest use of this descriptor is that it detects the patterns with the direction of the movement of the captured picture and hence it makes the job easy to train the pictures using the SVM and get the human detecte.