{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:17:08Z","timestamp":1777706228590,"version":"3.51.4"},"reference-count":10,"publisher":"SAGE Publications","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2022,7,21]]},"abstract":"<jats:p>The dynamic gesture trajectory recognition results are low accurate and poor real-time due to the problems of occlusion, complex background and fast gesture movement. In this paper, we take advantage of the advantages of machine vision to extract the video keyframes by the three-frame differential method and use the annotation software to produce the dataset. The you only look once 4 (YOLOv4) algorithm is improved to reduce the redundancy of the network structure and enhance the applicability of the feature map for hand gesture recognition. Combined with the Deep-sort real-time tracking feature, the hand motion trajectory is obtained by introducing the epiphenomenal features to effectively avoid the situation that the object is not tracked when it is obscured. To avoid the problem of gradient disappearance during deep network training, the DenseNet-BC-169 network is used to balance the recognition rate and training time for gesture trajectory classification. Compared with FLIXT, the winner of the dynamic gesture recognition challenge, the final results showed a 6.13% improvement in accuracy and video processing with the IsoGD dataset reached 31fps, validating the effectiveness of this method.<\/jats:p>","DOI":"10.3233\/jifs-212766","type":"journal-article","created":{"date-parts":[[2022,3,22]],"date-time":"2022-03-22T18:20:28Z","timestamp":1647973228000},"page":"2597-2607","source":"Crossref","is-referenced-by-count":1,"title":["Dynamic gesture recognition based on YOLOv4 and deep-sort methodological research"],"prefix":"10.1177","volume":"43","author":[{"given":"Dongjie","family":"Li","sequence":"first","affiliation":[{"name":"Heilongjiang Key Laboratory of Complex Intelligent System and Integration, Harbin University of Science and Technology, Harbin, China"}]},{"given":"Zilei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Heilongjiang Key Laboratory of Complex Intelligent System and Integration, Harbin University of Science and Technology, Harbin, China"}]},{"given":"Hongyue","family":"Zhao","sequence":"additional","affiliation":[{"name":"Heilongjiang Key Laboratory of Complex Intelligent System and Integration, Harbin University of Science and Technology, Harbin, China"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-212766_ref1","first-page":"1","article-title":"A review of hand gesture and sign language recognition techniques","volume":"10","author":"Ming","year":"2019","journal-title":"International Journal of Machine Learning and Cybernetics"},{"key":"10.3233\/JIFS-212766_ref2","doi-asserted-by":"crossref","unstructured":"Peng Y , Tao H. , Li W. , Yuan H. and Li T. , Dynamic Gesture Recognition Based on Feature Fusion Network and Variant ConvLSTM, IET Image Processing 14(2) (2020).","DOI":"10.1049\/iet-ipr.2019.1248"},{"key":"10.3233\/JIFS-212766_ref3","first-page":"76","article-title":"A System Recognizing Chinese Finger-Sprlling alphabets Based on data-Glove Input,","volume":"1","author":"Wu","year":"1999","journal-title":"Pattern Recognition and Artificial Intelligence"},{"key":"10.3233\/JIFS-212766_ref5","doi-asserted-by":"crossref","first-page":"6425","DOI":"10.1109\/JSEN.2016.2581023","article-title":"A Continuous Hand Gestures Recognition Technique for Human-Machine Interaction Using Accelerometer and Gyroscope Sensors","volume":"16","author":"Gupta","year":"2016","journal-title":"IEEE Sensors Journal"},{"key":"10.3233\/JIFS-212766_ref6","first-page":"167","article-title":"Research on Virtual Human Hand Motion Modeling System Based on Date Glove,","volume":"5","author":"Ge","year":"2020","journal-title":"Electronic Design Engineering"},{"key":"10.3233\/JIFS-212766_ref7","doi-asserted-by":"crossref","first-page":"052048","DOI":"10.1088\/1757-899X\/971\/5\/052048","article-title":"Use of convolutional neural net-works for segmenting images of roads from satellite","volume":"971","author":"Seliverstov","year":"2020","journal-title":"IOP Conference Series Materials Science and Engineering"},{"key":"10.3233\/JIFS-212766_ref8","doi-asserted-by":"crossref","first-page":"2302","DOI":"10.3724\/SP.J.1001.2008.02302","article-title":"Recognition of Complex Dynamic Gesture Based on HMM-FNN Model: Recognition of Complex Dynamic Gesture Based on HMM-FNN Model","volume":"19","author":"Wang","year":"2008","journal-title":"Journal of Software"},{"key":"10.3233\/JIFS-212766_ref9","first-page":"1","article-title":"3D Skeletal Gesture Recognition via Hidden States Exploration","volume":"99","author":"Liu","year":"2020","journal-title":"IEEE Transactions on Image Processing"},{"issue":"3","key":"10.3233\/JIFS-212766_ref10","doi-asserted-by":"crossref","first-page":"4405","DOI":"10.3233\/JIFS-200385","article-title":"Hand Gesture Recognition via Image Processing Techniques and Deep CNN","volume":"39","author":"Chung","year":"2020","journal-title":"Journal of Intelligent & Fuzzy Systems"},{"key":"10.3233\/JIFS-212766_ref16","doi-asserted-by":"crossref","first-page":"107416","DOI":"10.1016\/j.patcog.2020.107416","article-title":"Gesture recognition based on deep deformable 3D convolutional neural networks,","volume":"107","author":"Zhang","year":"2020","journal-title":"Pattern Recognition"}],"container-title":["Journal of Intelligent &amp; 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