aUToTrack: A Lightweight Object Detection and Tracking System for the SAE AutoDrive Challenge
| Autor: | Timothy D. Barfoot, Angela P. Schoellig, Sepehr Samavi, Steven L. Waslander, Keenan Burnett |
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| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Ground truth business.industry Computer science Tracking system 02 engineering and technology Pedestrian 010501 environmental sciences 01 natural sciences Object detection Computer Science - Robotics Inertial measurement unit Video tracking 0202 electrical engineering electronic engineering information engineering Benchmark (computing) Global Positioning System 020201 artificial intelligence & image processing Computer vision Artificial intelligence business Robotics (cs.RO) 0105 earth and related environmental sciences |
| Zdroj: | CRV |
| DOI: | 10.48550/arxiv.1905.08758 |
| Popis: | The University of Toronto is one of eight teams competing in the SAE AutoDrive Challenge -- a competition to develop a self-driving car by 2020. After placing first at the Year 1 challenge, we are headed to MCity in June 2019 for the second challenge. There, we will interact with pedestrians, cyclists, and cars. For safe operation, it is critical to have an accurate estimate of the position of all objects surrounding the vehicle. The contributions of this work are twofold: First, we present a new object detection and tracking dataset (UofTPed50), which uses GPS to ground truth the position and velocity of a pedestrian. To our knowledge, a dataset of this type for pedestrians has not been shown in the literature before. Second, we present a lightweight object detection and tracking system (aUToTrack) that uses vision, LIDAR, and GPS/IMU positioning to achieve state-of-the-art performance on the KITTI Object Tracking benchmark. We show that aUToTrack accurately estimates the position and velocity of pedestrians, in real-time, using CPUs only. aUToTrack has been tested in closed-loop experiments on a real self-driving car, and we demonstrate its performance on our dataset. Comment: Accepted to CRV (Computer and Robot Vision) 2019 |
| Databáze: | OpenAIRE |
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