2020/04/10 by Mohamed Chaabane, Chaabane, Mohamed, Lionel Gueguen +9
Computer Science · Engineering · #Advanced Neural Network Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Robotics and Sensor-Based Localization #Video Surveillance and Tracking Methods #cs.CV
paper · pdf · doi:10.48550/arxiv.2004.05232
WACV 2021
openalex publication_date 2020/04/10 · openalex created_date 2020/04/17 · arxiv created 2021/01/03 · arxiv updated 2021/01/05 · openalex updated_date 2026/07/28
Accurately estimating the position of static objects, such as traffic lights, from the moving camera of a self-driving car is a challenging problem. In this work, we present a system that improves the localization of static objects by jointly-optimizing the components of the system via learning. Our system is comprised of networks that perform: 1) 5DoF object pose estimation from a single image, 2) association of objects between pairs of frames, and 3) multi-object tracking to produce the final geo-localization of the static objects within the scene. We evaluate our approach using a publicly-available data set, focusing on traffic lights due to data availability. For each component, we compare against contemporary alternatives and show significantly-improved performance. We also show that the end-to-end system performance is further improved via joint-training of the constituent models.