Robust Autonomous Car-Like Robot Tracking Based on Tracking-Learning-Detection

Abstract:

This paper proposes a robust tracking algorithm for an autonomous car-like robot, and this algorithm is based on the Tracking-Learning-Detection (TLD). In this paper, the TLD method is extended to track the autonomous car-like robot for the first time. In order to improve accuracy and robustness of the proposed algorithm, a method of symmetry detection of autonomous car-like robot rear is integrated into the TLD. Moreover, the Median-Flow tracker in TLD is improved with a pyramid-based optical flow tracking method to capture fast moving objects. Extensive experiments and comparisons show the robustness of the proposed method.

• Periodical: Applied Mechanics and Materials (Volumes 687-691)
• Pages: 564-571
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.687-691.564
• Online since: November 2014
• Authors: Lin Bao Xu*, Shu Ming Tang, Jin Feng Yang, Yan Min Dong
• Keywords: Autonomous Car-Like Robot Tracking, On-Line Learning, Pyramid-Based Optical Flow, Symmetry Detection, Tracking-Learning-Detection

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