Joint Object Tracking and Segmentation with Independent Convolutional Neural Networks

Abstract

Object tracking and segmentation are important research topics in computer vision. They provide the trajectory and boundary of an object based on their appearance and shape features. Most studies on tracking and segmentation focus on encoding methods for the feature of an object. However, the tracking trajectory and segmentation mask are acquired separately, although similar visual information is required for both methods. Therefore, in this paper, we propose a CNN-based joint object tracking and segmentation framework that provides a segmentation mask while improving the performance of object tacker. In our model, the tracking model determines the trajectory of the target object as a bounding box in each frame. Given the bounding box at each frame, the segmentation model predicts a dense mask of the target object in the bounding box. Then, the segmentation mask is used to refine the bounding box for the tracking model. We evaluate the performance of our algorithm on DAVIS benchmark dataset by AUC score and mean IoU. We showed that the performance of original tracker was improved by our proposed framework.