Double Residual Network Recognition Method for Falling Abnormal Behavior

Abstract

In the abnormal behavior monitoring, due to the complicated situation such as monitoring angles of view, human body postures and scenes, it is easy to cause vanishing gradient and over-fitting by directly adding 3D con-volutional neural network layers to extract effective visual features, which reduces the action recognition rate. To solve these problems, this paper proposes a fall recognition method based on the double residual convolutional network. By nesting the residual network in the residual network, the double residual network fully integrates shallow and deep visual features and alleviates the impact of the vanishing gradient, and makes the performance of residual network improved. Finally, multiple cameras fall dataset (MCFD) and UR fall dataset (URFD) are tested and evaluated by the 5-fold cross-validation method. The results show that the performance is better than some fall recognition methods based on 3D convolutional network (C3D), 3D residual network (3D-Resnet), Pseudo-3D residual network (P3D), and (2+1)D residual network (R(2+1)D), which verifies the effectiveness of the double residual network model for improving the abnormal behavior recognition.

Keywords

• fall recognition
• residual network
• vanishing gradient
• action recognition