CNN-Based Mask-Pose Fusion for Detecting Specific Persons on Heterogeneous Embedded Systems
| Autor: | Hyun Kim, Jinhong Lee, Jeongjun Lee, Ji Hoon Jang, Dayoung Chun |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
General Computer Science
business.industry Computer science Semantics (computer science) NVIDIA Jetson AGX Xavier AlphaPose General Engineering deep learning object detection Image segmentation Object (computer science) Convolutional neural network Object detection TK1-9971 Parallel processing (DSP implementation) instance segmentation embedded systems General Materials Science Computer vision Segmentation Electrical engineering. Electronics. Nuclear engineering Artificial intelligence business Pose |
| Zdroj: | IEEE Access, Vol 9, Pp 120358-120366 (2021) |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/access.2021.3108776 |
| Popis: | In recent times, numerous convolutional neural network (CNN) based detection models have been proposed and have shown excellent performance. However, because these models are generally developed to detect objects in class units (e.g., person, car), additional training processes with numerous datasets are required to find a specific object. This paper proposes a model that accurately detects specific persons by using top clothing color information without any additional training processes. The proposed method combines CNN-based instance segmentation and pose estimation, utilizing all the advantages of each technique. To avoid redundant computations, these two schemes are implemented as a filtering-based sequential operation structure. As a result, the proposed method has a 92.57% of accuracy in detecting a specific person with only a slight processing speed decrease. Furthermore, in this paper, the proposed model is efficiently ported on the heterogeneous embedded platform (i.e., NVIDIA Jetson AGX Xavier) with a parallel processing technique to maximize the hardware utilization. |
| Databáze: | OpenAIRE |
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