Guided MDNet tracker with guided samples

Abstract

Visual tracking is the process of estimating the position of an object in a video sequence and plays a very important role in the field of autonomous video processing. Recent work renders that the trackers developed using deep learning techniques such as the convolutional neural network (CNN) exhibits outstanding performances in terms of accuracy and robustness as compared to other state-of-the-art trackers. Multi-domain convolutional neural network (MDNet) is a deep tracker which uses the CNN for estimating the target in each frame of the video sequence. The majority of the tracking challenges could be very easily handled by the MDNet tracker due to its offline training and online tracking features. The offline training stage helps in capturing the target representations into the shared layers of the CNN, while the online tracking uses a large number of probable random samples of bboxes (bounding boxes) around the previous target for estimating the target in the current frame. Once the target is estimated, a process of fine-tuning is performed which will update the weights of shared layers of CNN. The large number of random samples used for target estimation and the huge number of random training samples generated for the fine-tuning during the online stage makes tracking by MDNet computationally complex and slow. The major contribution of this paper is to suggest using guided samples to the input of the CNN rather than random samples. Moreover, it generates a lesser number of highly efficient training samples for the fine-tuning which helps in decreasing the computational complexity of the tracker by half without much compromise on the performance and thus improves the speed of the tracking process. An extensive evaluation has been performed on the proposed Guided MDNet with different datasets like ALOV300++, OTB and VOT, and its performances are measured in terms of metrics like F-score, one-pass evaluation, robustness and accuracy.