Object Detection and Tracking Based on Deep Learning

Object detection and tracking is an exciting and interesting research area in the field of computer vision, and its technologies have been widely used in various applications such as surveillance, military and augmented reality. This paper suggests and implements a robust object detection and tracking scheme to localize and to track multiple objects from input images, which estimates target state using the likelihoods obtained from convolutional neural networks. As the experimental results, the proposed system is effective to handle multiple target appearances and other exceptions, and it is able to detect the interesting object accurately in various environments, compared to the traditional method.