2020/02/19 by Irene Cortes, Irene Cortés, Jorge Beltrán +8
Computer Science · Engineering · #Advanced Neural Network Applications #Artificial intelligence #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Computer vision #Detector #FOS: Computer and information sciences #Geography #Identification (biology) #Industrial Vision Systems and Defect Detection #Lidar #Object (grammar) #Object detection #Pattern recognition (psychology) #Pipeline (software) #Real-time computing #Remote sensing #Telecommunications #Video Surveillance and Tracking Methods #cs.CV
paper · pdf · doi:10.48550/arxiv.2002.08239
Submitted to IEEE Intelligent Vehicles Symposium 2020 (IV2020)
arxiv created 2020/02/19 · openalex publication_date 2020/02/19 · arxiv updated 2020/02/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The rapid development of embedded hardware in autonomous vehicles broadens their computational capabilities, thus bringing the possibility to mount more complete sensor setups able to handle driving scenarios of higher complexity. As a result, new challenges such as multiple detections of the same object have to be addressed. In this work, a siamese network is integrated into the pipeline of a well-known 3D object detector approach to suppress duplicate proposals coming from different cameras via re-identification. Additionally, associations are exploited to enhance the 3D box regression of the object by aggregating their corresponding LiDAR frustums. The experimental evaluation on the nuScenes dataset shows that the proposed method outperforms traditional NMS approaches.