![]() ![]() Tracking-based semi-supervised learning, as originally presented at RSS2011, was an offline algorithm. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2013. Project Page - Supplementary material, C++ code, poster, presentation, We currently use this method to track all dynamic obstacles seen by our autonomous vehicle, in real-time, with significantly improved accuracy compared to our previous Kalman-filter based approach. ![]() The algorithm is “anytime”, allowing speed or accuracy to be optimized based on the needs of the application. Now, computational time is allocated dynamically according to the shape of the track’s posterior distribution. Improving on our ICRA 2013 paper, this new approach enables real-time probabilistic object tracking. Robotics: Science and Systems (RSS), 2014. Combining 3D Shape, Color, and Motion for Robust Anytime Trackingĭavid Held, Jesse Levinson, Sebastian Thrun, Silvio Savarese. ![]()
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