An intelligent deep learning based capsule network model for human detection in indoor surveillance videos

At present times, indoor surveillance becomes a hot research topic among researchers and business sectors. Human detection is one of the vital areas of focus in the surveillance system owing to its significance in proper person detection, human activity identification, and scene classification. Since the indoor spaces comprise poor lighting, variable illuminations, shadowing, and complex background, the human detection process becomes a tedious task. The advent of computer vision and deep learning (DL) models is commonly employed for human detection. This article presents a new intelligent deep learning model for human detection in indoor surveillance videos (IDL-HDIS). As data augmentation process is one of the most renowned ways to increase the size of the dataset which is highly essential for enhancing the prediction accuracy of the model, the same is carried out as a part of even this research work which includes performing rotation, translation and flipping. The IDL-GDIS model uses Faster Region Convolutional Neural Network (Faster R-CNN) model for human detection. The Faster R-CNN comprises of Fast R-CNN and Region Proposal Network (RPN). The RPN uses Capsule Networks (CapsNet) model as a shared convolution neural network (CNN), which acts as a feature extractor and generates the feature map. Besides, dropout is employed to avoid overfitting problem in the CapsNet architecture, the validation of IDL-HDIS model is done by a comprehensive simulation analysis under different aspects. The validation is supported by the evident results of the IDL-HDIS model which is given in the paper.