Computational Model Based on Neural Network of Visual Cortex for Human Action Recognition
文献类型：期刊论文
| 作者 | Liu, Haihua1,2,3; Shu, Na1; Tang, Qiling1; Zhang, Wensheng4
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| 刊名 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
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| 出版日期 | 2018-05-01 |
| 卷号 | 29期号:5页码:1427-1440 |
| 关键词 | Action Recognition Classical Receptive Field (Rf) Spiking Neural Networks (Snns) Surround Suppression Visual Cortex |
| DOI | 10.1109/TNNLS.2017.2669522 |
| 文献子类 | Article |
| 英文摘要 | In this paper, we propose a bioinspired model for human action recognition through modeling neural mechanisms of information processing in two visual cortical areas: the primary visual cortex (V1) and the middle temporal cortex (MT) dedicated to motion. This model, named V1-MT, is composed of V1 and MT models (layers) corresponding to their cortical areas, which are built with layered spiking neural networks (SNNs). Some neuron properties in V1 and MT, such as direction and speed selectivity, spatiotemporal inseparability, and center surround suppression, are integrated into SNNs. Based on speed and direction selectivity, V1 and MT models contain multiple SNN channels, each of which processes motion information in sequences with spatiotemporal tunings of neurons at a certain speed and different directions. Therefore, we propose two operations, input signal perceiving with 3-D Gabor filters and surround inhibition processing with 3-D differences of Gaussian functions, to perform this task according to the spatiotemporal inseparability and center surround suppression of neurons. Then, neurons are modeled with our simplified integrate-and-fire model and motion information is transformed into spike trains. Afterward, we define a new feature vector: a mean motion map computed from spike trains in all channels to represent human actions. Finally, a support vector machine is trained to classify actions represented by the feature vectors. We conducted extensive experiments on public action databases, and the results show that our model outperforms other bioinspired models and rivals the state-of-the-art approaches. |
| WOS关键词 | CELL RECEPTIVE-FIELDS ; BIOLOGICAL MOTION ; SPATIOTEMPORAL ORGANIZATION ; GABOR FILTERS ; VISION SENSOR ; FEATURES ; ARCHITECTURE ; ENHANCEMENT ; SUPPRESSION ; SELECTIVITY |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000430729100003 |
| 资助机构 | National Natural Science Foundation of China(91320102 ; 60972158 ; 61432008 ; 61532006) |
| 源URL | [http://ir.ia.ac.cn/handle/173211/22032]  |
| 专题 | 精密感知与控制研究中心_人工智能与机器学习 |
| 作者单位 | 1.South Cent Univ Nationalities, Sch Biomed Engn, Wuhan 430074, Hubei, Peoples R China 2.Key Lab Cognit Sci State Ethn Affairs Commiss, Wuhan 430074, Hubei, Peoples R China 3.Hubei Key Lab Med Informat Anal & Tumor Diag & Tr, Wuhan 430074, Hubei, Peoples R China 4.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Haihua,Shu, Na,Tang, Qiling,et al. Computational Model Based on Neural Network of Visual Cortex for Human Action Recognition[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2018,29(5):1427-1440. |
| APA | Liu, Haihua,Shu, Na,Tang, Qiling,&Zhang, Wensheng.(2018).Computational Model Based on Neural Network of Visual Cortex for Human Action Recognition.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,29(5),1427-1440. |
| MLA | Liu, Haihua,et al."Computational Model Based on Neural Network of Visual Cortex for Human Action Recognition".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 29.5(2018):1427-1440. |
入库方式： OAI收割
来源：自动化研究所
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