Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
Enquiries about data availability should be directed to the authors.
References
An F, Liu Z (2019) Facial expression recognition algorithm based on parameter adaptive initialization of CNN and LSTM. Vis Comput 35:1–16
Chahyati D, Fanany MI, Arymurthy AM (2017) Tracking people by detection using CNN features. Proc Comput Sci 124:167–172
Flores Calero MJ, Aldás M, Lázaro J, Gardel A, Onofa N, Quinga B (2019) Pedestrian detection under partial occlusion by using logic inference, HOG and SVM. IEEE Lat Am Trans 17(09):1552–1559
Guo K, Wu S, Xu YF (2017) Face recognition using both visible light image and near-infrared image and a deep network. CAAI Trans Intell Technol 2(1):39–47
Hahn S, Choi H (2020) Understanding dropout as an optimization trick. Neurocomputing 398:64–70
Haq EU, Jianjun H, Li K, Haq HU (2020) Human detection and tracking with deep convolutional neural networks under the constrained of noise and occluded scenes. Multimed Tools Appl 79(41):30685–30708
Jeon H, Nguyen VD, Jeon JW (2019) Pedestrian detection based on deep learning. In: IECON—45th annual conference of the IEEE industrial electronics society, Lisbon, pp 144–151
Kim B, Yuvaraj N, Sri Preethaa KR, Santhosh R, Sabari A (2020) Enhanced pedestrian detection using optimized deep convolution neural network for smart building surveillance. Soft Comput 24(22):17081–17092
Kundid Vasić M, Papić V (2020) Multimodel deep learning for person detection in aerial images. Electronics 9(9):1459
Lv JJ, Cheng C, Tian GD, Zhou XD, Zhou X (2016) Landmark perturbation-based data augmentation for unconstrained face recognition. Signal Process Image Commun 47:465–475
Mateus A, Ribeiro D, Miraldo P, Nascimento JC (2019) Efficient and robust Pedestrian Detection using deep learning for human-aware navigation. Robot Auton Syst 113:23–37
Mekhalfi ML, Bejiga MB, Soresina D, Melgani F, Demir B (2019) Capsule networks for object detection in UAV imagery. Remote Sens 11(14):1694
Patrick MK, Adekoya AF, Mighty AA, Edward BY (2019) Capsule networks–a survey. J King Saud Univ Comput Inf Sci 34(1):1295–1310
Ren S, He K, Girshick R, Sun J (2016) Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137–1149
Said YF, Barr M (2019) Pedestrian detection for advanced driver assistance systems using deep learning algorithms. IJCSNS Int J Comput Sci Netw Secur 19(10):9–14
Sulman N, Sanocki T, Goldgof D, Kasturi R (2008) How effective is human video surveillance performance? In: 19th international conference on pattern recognition (ICPR 2008), IEEE, Piscataway, pp 1–3
Supreeth HSG, Patil CM (2018) Efficient multiple moving object detection and tracking using combined background subtraction and clustering. Signal Image Video Process 15:1097
Xinxin S, Liangnian J, Qinghua L (2019) Detection of stationary humans using time-division UWB MIMO through-wall radar. J Eng 20:6799–6802
Funding
The authors have not disclosed any funding.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that there has no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Ushasukhanya, S., Malleswari, T.Y.J.N., Karthikeyan, M. et al. An intelligent deep learning based capsule network model for human detection in indoor surveillance videos. Soft Comput 28, 737–747 (2024). https://doi.org/10.1007/s00500-023-09443-8
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-023-09443-8