| dc.description.abstract | Visual Object Tracking is a popular task in computer vision and deep learning. The purpose of object tracking is to find the location of the target object from a series of continuous images. These years, most object tracking method use deep learning to improve the accuracy. In the field of deep learning, object tracking can be divided into single object tracking and multi-object tracking, the former aims to find the location of the target object in each frames, while the later aims to do the object association, which matches the objects in different time steps. This paper will focus on single object tracking.
Most of the current deep learning based single object tracking methods use Siamese network architecture, then using the correlation filter to find the correlation between target image and search image. This paper try to improve some existing problems in Siamese based visual object tracking method. We try to add variance loss to enhance the model to distinguish the foreground and the background. Besides, we add the graph convolutional network to improve the accuracy by associating the target object and the surrounding objects.
Object detection model is to determine whether the target object exists in the image for each input image, but in a continuous series of frames, each frame is slightly different, some objects may be miss detected in some frames, so we try to use tracking model to solve the problem. When the detection model detect the target object, we can use tracking model to track the target in the later frames. We use the visual object tracking model to enhance the stability and the accuracy of the object detection model. | en_US |