Big Data Analysis for Industrial Activity Recognition Using Attention-Inspired Sequential Temporal Convolution Network

Deep-learning-based human activity recognition (HAR) methods have significantly transformed a wide range of domains over recent years. However, the adoption of Big Data techniques in industrial applications remains challenging due to issues such as generalized weight optimization, diverse viewpoints, and the complex spatiotemporal features of videos. To address these challenges, this work presents an industrial HAR framework consisting of two main phases. First, a squeeze bottleneck attention block (SBAB) is introduced to enhance the learning capabilities of the backbone model for contextual learning, which allows for the selection and refinement of an optimal feature vector. In the second phase, we propose an effective sequential temporal convolutional network (STCN), which is designed in parallel fashion to mitigate the issues of exploding and vanishing gradients associated with sequence learning. The high-dimensional spatiotemporal feature vectors from the STCN undergo further refinement through our proposed SBAB in a sequential manner, to optimize the features for HAR and enhance the overall performance. The efficacy of the proposed framework is validated through extensive experiments on six datasets, including data from industrial and general activities.