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@article{197424,
        author = {Mr. Mohammed MazherUddin and Kaif Khan and Ayan Saleem and Mohammed Junaid},
        title = {Hybrid Multiscale Deep Learning Framework for Human Behaviour Recognition Integrating CNN, GRU and Bidirectional Temporal Modelling},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {6267-6273},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197424},
        abstract = {The key problem in human behaviour recognition is how to build a spatiotemporal feature extraction and classification network. Aiming at the problem that the existing channel attention mechanism directly pools the global average information of each channel and ignores its local spatial information, this paper proposes two improved channel attention modules, namely the space-time (ST) interaction module of matrix operation and the depth separable convolution module, combined with the research of human behaviour recognition. Combined with the superior performance of convolutional neural network (CNN) in image and video processing, a multi-scale convolutional neural network method for human behaviour recognition is proposed. Firstly, the behavior video is segmented, and low rank learning is performed on each video segment to extract the corresponding Low rank behavior information, and then this Low rank behavior information are connected on the time axis to obtain the Low rank behavior information of the whole video, so as to effectively capture the behavior information in the video, avoiding tedious extraction steps and various assumptions. The ability of neural network to model human behavior can be transferred and reused in networks with different structures. According to the different characteristics of data features at different network levels, two effective feature difference measurement functions are introduced to reduce the difference between features extracted from different network structures. Experiments on several public datasets show that the proposed method has a good classification effect. The experimental results show that the method has a good accuracy in human behavior recognition. It is proved that the proposed model not only improves the recognition accuracy, but also effectively reduces the computational complexity of output weights and improves the compactness of the model structure.},
        keywords = {Human Behaviour Recognition, Multiscale CNN, 3D Convolutional Neural Network, GRU, Bidirectional LSTM, Deep Learning, UCI HAR Dataset, Spatiotemporal Feature Extraction, Activity Recognition.},
        month = {April},
        }