Moving Object Detection with Deep CNNS

Abstract

Object detection has been playing an increasingly important role in many fields in recent years. Object detection is a still challenging task because, for the group of people, each individual has his unique appearance and, body shape. Compared with the traditional method, the deep learning neural network has the advantages of shorter computing time, higher accuracy and easier operation. Therefore, deep learning met classifichod has been widely used in object detection. The current state of art in object detection is Retina Net. Among all the deep learning approaches, Retina Net gives the highest accuracy of object detection (Lin, Goyal, Girshick, He, & Piotr Dollar, 2018).The temporal component of video provides additional and significant clues as compared to the static image. In this paper, the temporal relationship of the images is utilized to improve the accuracy of object detection. Compared to using only an image, the accuracy of object detection is 21.4% higher when a sequence of images is applied.