3D Object Detection and Tracking Based on Streaming Data
| Autor: | Long Cheng, Xusen Guo, Chengzhang Yang, Kai Huang, Guo Silu, Jianfeng Gu, Xu Zixiao, Shanghua Liu |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer science business.industry Deep learning Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition 02 engineering and technology Object detection Streaming data Video tracking 0202 electrical engineering electronic engineering information engineering Leverage (statistics) 020201 artificial intelligence & image processing Computer vision Artificial intelligence business |
| Zdroj: | ICRA |
| DOI: | 10.48550/arxiv.2009.06169 |
| Popis: | Recent approaches for 3D object detection have made tremendous progresses due to the development of deep learning. However, previous researches are mostly based on individual frames, leading to limited exploitation of information between frames. In this paper, we attempt to leverage the temporal information in streaming data and explore 3D streaming based object detection as well as tracking. Toward this goal, we set up a dual-way network for 3D object detection based on keyframes, and then propagate predictions to non-key frames through a motion based interpolation algorithm guided by temporal information. Our framework is not only shown to have significant improvements on object detection compared with frame-by-frame paradigm, but also proven to produce competitive results on KITTI Object Tracking Benchmark, with 76.68% in MOTA and 81.65% in MOTP respectively. Comment: Accepted by ICRA 2020 |
| Databáze: | OpenAIRE |
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