Human Behavior Recognition using MCNN

Abstract - Human behavior recognition needs an efficient spatiotemporal feature extraction and classification model. However, the current channel attention mechanism depends on global average pooling and ignores local spatial information. To overcome this issue, this paper proposes two enhanced channel attention modules: a Space-Time (ST) interaction module using matrix computation and a depth-wise separable convolution module. A multi-scale convolutional neural network is constructed to improve feature representation with lower computational complexity. Here we are using 3D CNN for more accurate predictions or recognition. The input image dataset is divided into multiple segments, and low-rank learning is performed on each segment to extract concise behavior features. The features are stacked along the temporal dimension to acquire comprehensive image representations. Moreover, two functions for feature difference measurement are presented to eliminate differences in features derived from different network architectures, facilitating effective feature transfer. The proposed approach has been evaluated on public benchmarks, and the results demonstrate that the approach improves classification accuracy with lower computational complexity. The proposed model improves recognition accuracy while preserving structural compactness. This approach is more accurate when compared with the existing approaches.