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The brightness and contrast of the display thus interfere with the recognition results. Therefore, traditional algorithms are easily affected by the lighting environment around the recognition object. To improve the accuracy and recognition rate of the behavior recognition algorithm in different lighting environments, a convolutional neural network (CNN) algorithm using a dual-stream method of time flow and spatial flow is studied here. First, we collect behavioral action data sets and preprocess the data. The core of the behavior recognition algorithm of the dual-stream residual CNN is to use the time stream and the spatial stream to fuse behavioral features and eliminate meaningless data features. After processing Perform feature selection on the data, select the acoustic wave and light-sensing features of the data, and finally, use the extracted features to classify and identify using the two-stream residual CNN and the traditional behavior recognition method. The behavior recognition algorithm based on the dual-stream residual CNN was tested on the data of four groups of people. For the behavioral feature map with a data volume of 50, the behavior recognition algorithm of the dual-stream residual CNN was effective in various environments under different lighting conditions. The recognition accuracy can reach 83.5%, which is 12.3% higher than the traditional. The behavior recognition algorithm of the dual-stream residual CNN takes 17.25\u2009s less than the conventional recognition algorithm. It is concluded that behavior recognition based on dual-stream residual CNNs can indeed improve the recognition accuracy and recognition speed in environments with different lighting conditions than traditional behavior recognition.<\/jats:p>","DOI":"10.1515\/jisys-2024-0239","type":"journal-article","created":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T02:11:01Z","timestamp":1730859061000},"source":"Crossref","is-referenced-by-count":1,"title":["Behavior recognition algorithm based on a dual-stream residual convolutional neural network"],"prefix":"10.1515","volume":"33","author":[{"given":"Dawei","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Information Engineering, Liaodong University , Dandong , 118000, Liaoning , China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2024,11,6]]},"reference":[{"key":"2025120517212256392_j_jisys-2024-0239_ref_001","unstructured":"Liu L. 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