CASIA OpenIR  > 精密感知与控制研究中心  > 人工智能与机器学习
Computational Model Based on Neural Network of Visual Cortex for Human Action Recognition
Liu, Haihua1,2,3; Shu, Na1; Tang, Qiling1; Zhang, Wensheng4
Source PublicationIEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
2018-05-01
Volume29Issue:5Pages:1427-1440
SubtypeArticle
AbstractIn 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.
KeywordAction Recognition Classical Receptive Field (Rf) Spiking Neural Networks (Snns) Surround Suppression Visual Cortex
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TNNLS.2017.2669522
WOS KeywordCELL RECEPTIVE-FIELDS ; BIOLOGICAL MOTION ; SPATIOTEMPORAL ORGANIZATION ; GABOR FILTERS ; VISION SENSOR ; FEATURES ; ARCHITECTURE ; ENHANCEMENT ; SUPPRESSION ; SELECTIVITY
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(91320102 ; 60972158 ; 61432008 ; 61532006)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000430729100003
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/22032
Collection精密感知与控制研究中心_人工智能与机器学习
Affiliation1.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
Recommended Citation
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.
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