Real-time Visual Tracking Based on Convolutional Neural Networks

Abstract

Traditional target tracking is based on target detection. When the target changes significantly, such as occlusion, scale change, the update of the tracking model will waste a lot of space and time resources, resulting in a very slow tracking speed, which cannot meet the actual engineering needs. In view of the above situation, an end-to-end tracking strategy is proposed, which is simpler and faster than the existing technology. The proposed tracker only needs to detect the first frame image and use it as the input of the model, and set the multi-task loss function to predict the position of the next frame of the target and the size of the bounding box. This paper constructs a lightweight network architecture with an additional selection mechanism to avoid wasting resources for global search and matching. Through experiments, good results can be achieved on the standard data set, and tracking speeds close to one hundred frames per second are achieved, which is very competitive with existing advanced trackers.