INVESTIGATION OF CONVOLUTIONAL NEURAL NETWORKS FOR VISUAL TRACKING OF PEDESTRIANS

The problem of human detection in an image or video sequence is still a hot topic nowadays. It has been actively researched and still, the accurate and fast detection remains an issue. This paper aims to provide additional insights into existing solutions for pedestrian detection. The proposed method is to only use a part of video frames for object detection showed that it is possible to receive 88 % processing speed increase without accuracy lost when using every second frame. However, skipping more frames introduces tracking latency of approximated location of a pedestrian.