DTR-HAR: deep temporal residual representation for human activity recognition

Human activity recognition (HAR) is a highly prized application in the pattern recognition and the computer vision fields. Up till now, deep neural networks have acquired big attention in computer studies and image processing fields, and have generated significant results. In this paper, we propose a deep temporal residual system for daily living activity recognition that aims to enhance spatiotemporal feature representation in order to improve the HAR system performance. To this end, we adopt a deep residual convolutional neural network (RCN) to retain discriminative visual features relayed to appearance and long short-term memory neural network to capture the long-term temporal evolution of actions. The latter was considered to implement time dependencies occurring when carrying out the activity to enhance features extracted from the RCN network by adding time information to address the dynamic activity recognition problem as a sequence labeling job. The deep temporal residual model for human activity recognition system is performed on two benchmark publicly available datasets: MSRDailyActivity3D and CAD-60. the proposed system achieves very competitive results when compared to others from the state of the art.

Automated summary

This paper introduces a deep temporal residual system for enhancing human activity recognition performance, combining a deep residual convolutional neural network with long short-term memory neural network for improved spatiotemporal feature representation.