A Review of Visual Trackers and Analysis of its Application to Mobile Robot

Abstract

Computer vision has received a significant attention in recent year, which is one of the important parts for robots to obtain information about the external environment. Visual trackers can provide the necessary physical and environmental parameters for the mobile robot, and their performance is related to the actual application of the robot. This study provides a comprehensive survey on visual trackers. Following a brief introduction, we first analyzed the basic framework and difficulties of visual trackers. Then the structure of generative and discriminative methods is introduced, and summarized the feature descriptors, modeling methods, and learning methods which be used in tracker. Later we reviewed and evaluated the state-of-the-art progress on discriminative trackers from three directions: correlation filter, deep learning and convolutional features. Finally, we analyzed the research direction of visual tracker used in mobile robot, as well as outlined the future trends for visual tracker on mobile robot.

References in corpus (10)

• High-Speed Tracking with Kernelized Correlation Filters
• Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking
• Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network
• Modeling and Propagating CNNs in a Tree Structure for Visual Tracking
• Large Margin Object Tracking with Circulant Feature Maps
• Learning Background-Aware Correlation Filters for Visual Tracking
• Understanding and Diagnosing Visual Tracking Systems
• Learning Policies for Adaptive Tracking with Deep Feature Cascades
• Recurrent Filter Learning for Visual Tracking
• DCCO: Towards Deformable Continuous Convolution Operators