Abstract-Although object tracking has been studied for decades, real-time tracking algorithms often suffer from low accuracy and poor robustness when confronted with difficult, realworld data. We present a tracker that combines 3D shape, color (when available), and motion cues to accurately track moving objects in real-time. Our tracker allocates computational effort based on the shape of the posterior distribution. Starting with a coarse approximation to the posterior, the tracker successively refines this distribution, increasing in tracking accuracy over time. The tracker can thus be run for any amount of time, after which the current approximation to the posterior is returned. Even at a minimum runtime of 0.7 milliseconds, our method outperforms all of the baseline methods of similar speed by at least 10%. If our tracker is allowed to run for longer, the accuracy continues to improve, and it continues to outperform all baseline methods. Our tracker is thus anytime, allowing the speed or accuracy to be optimized based on the needs of the application. Many robotics applications are limited in what they can achieve due to unreliable tracking estimates. For example, an autonomous vehicle driving past a row of parked cars should know if one of these cars is about to pull out into the lane. Current state-of-the-art trackers give noisy estimates of the velocity of these vehicles, which are difficult to track due to heavy occlusion and viewpoint changes. Additionally, without robust estimates of the velocity of nearby vehicles, merging onto or off of highways or changing lanes become formidable tasks. Similar issues will be encountered by any robot that must act autonomously in crowded, dynamic environments. Our tracker makes use of the full 3D shape of the object being tracked, which allows us to robustly track objects despite occlusions or changes in viewpoint. We place the 3D shape information in a probabilistic framework, in which we combine cues from shape, color, and motion. As we will show, adding color and motion information gives a large benefit to our system compared to using the 3D shape alone. This information is especially useful for distant objects or objects under heavy occlusions, when detailed 3D shape information may not be available. We make use of a grid-based method to sample velocities from the state space. Traditional grid-based approaches are too slow to track multiple objects in real-time. We are able to finely sample from a large grid in real-time through the use of a novel method called annealed dynamic histograms. We start by sampling from the state space at a coarse resolution, using an approximation to the posterior distribution over velocities. As the sampling resolution increases, we anneal this distribution,
REF proposes to align LIDAR object candidates in a coarse-to-fine fashion using annealed dynamic histograms in order to obtain precise position and velocity.
Combining 3D Shape, Color, and Motion for Robust Anytime Tracking
Robotics: Science and Systems
Computer Science