Accurate People Counting Based on Radar: Deep Learning Approach
| Autor: | Jae-Ho Choi, Seung-Hyun Jin, Kyung-Tae Kim, Jieun Kim, Nam-Hoon Jeong |
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| Rok vydání: | 2020 |
| Předmět: |
020301 aerospace & aeronautics
Computer science business.industry Deep learning 010401 analytical chemistry Feature extraction 02 engineering and technology Overfitting computer.software_genre 01 natural sciences 0104 chemical sciences law.invention 0203 mechanical engineering law Clutter Data mining Artificial intelligence Radar business computer |
| Zdroj: | 2020 IEEE Radar Conference (RadarConf20). |
| DOI: | 10.1109/radarconf2043947.2020.9266496 |
| Popis: | In this study, a novel radar-based people counting (PC) method is presented using the deep learning (DL) approach. The DL algorithm is a great tool that enables the automatic formation of the optimal features; however, it must be utilized carefully, considering the domain knowledge to prevent the concerns of learning unnecessary information, followed by overfitting. To address the problem and successfully apply the DL framework to the radar-based PC, we propose three novel solutions. First, we establish the preprocessing pipelines to transform the raw signals into a suitable form for network inputs. Second, a network architecture is newly proposed considering the radar signal characteristics and PC application. Finally, we propose several data augmentation strategies to artificially increase the size of training data. It was observed from experiments using real measured data that the proposed DL-based PC approach outperforms the conventional PC methods. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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