Abstract
Human activity recognition (HAR) is a crucial component for many current applications, including those in the healthcare, security, and entertainment sectors. At the current state of the art, deep learning outperforms machine learning with its ability to automatically extract features. Autoencoders (AE) and convolutional neural networks (CNN) are the types of neural networks that are known for their good performance in dimensionality reduction and image classification, respectively. As most of the methods introduced for classification purposes are limited to sensor based methods. This paper mainly focuses on vision based HAR where we present a combination of AE and CNN for the classification of labeled data, in which convolutional AE (conv-AE) is utilized for two functions: dimensionality reduction and feature extraction and CNN is employed for classifying the activities. For the proposed model’s implementation, public benchmark datasets KTH and Weizmann are considered, on which we have attained a recognition rate of 96.3%, 94.89% for both, respectively. Comparative analysis is provided for the proposed model for the above-mentioned datasets.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Basly, H., Ouarda, W., Sayadi, F.E., Ouni, B., Alimi, A.M.: CNN-SVM Learning Approach based Human Activity Recognition, pp. 271–281. ICISP, Springer (2020)
Bouchabou, D., Nguyen, S.M., Lohr, C., LeDuc, B., Kanellos, I.: A survey of human activity recognition in smart homes based on IoT sensors algorithms: taxonomies, challenges, and opportunities with deep learning. Sensors, MDPI 21, 6037 (2021)
Zhang, S., et al.: Deep learning in human activity recognition with wearable sensors: a review on advances. Sensors, MDPI 4, 1476 (2022)
Alo, U.R., Nweke, H.F., The, Y.W., Murtaza, G.: Smartphone motion sensor-based complex human activity identification using deep stacked autoencoder algorithm for enhanced smart healthcare system. Sensors, MDPI 20, 6300 (2020)
Gu, F., Khoshelham, K., Valaee, S., Shang, J., Zhang, R.: Locomotion activity recognition using stacked denoising autoencoders. IEEE Internet of Things Journal, IEEE 5, 2085–2093 (2018)
Sunny, J.T., et al.: Applications and challenges of human activity recognition using sensors in a smart environment. IJIRST Int. J. Innov. Res. Sci. Technol 2, 50–57 (2015)
Kiruba, K., Shiloah, E.D., Sunil, R.R.C.: Hexagonal Volume Local Binary Pattern (H-VLBP) with Deep Stacked Autoencoder for Human Action Recognition. Cognitive Systems Research, Elsevier 58, 71–93 (2019)
Gnouma, M., Ladjailia, A., Ejbali, R., Zaied, M.: Stacked sparse autoencoder and history of binary motion image for human activity recognition. Multimedia Tools and Applications, Springer 78, 2157–2179 (2019)
Nigam, S., Singh, R., Singh, M.K., Singh, V.K.: Multiview human activity recognition using uniform rotation invariant local binary patterns. J. Ambient Intell. Humani. Comp. Springer, 1–19 (2022)
Song, X., Zhou, H., Liu, G.: Human behavior recognition based on multi-feature fusion of image. Cluster Computing, Springer 22, 9113–9121 (2019)
Ramya, P., Rajeswari, R.: Human action recognition using distance transform and entropy based features. Multimedia Tools and Applications, Springer 80, 8147–8173 (2021)
Mahmoud, R., Belgacem, S., Omri, M.N.: Towards an end-to-end Isolated and continuous deep gesture recognition process. Neural Computing and Applications, Springer 34, 13713–13732 (2022)
Karuppannan, K., Darmanayagam, S.E., Cyril, S.R.R.: Human action recognition using fusion-based discriminative features and long short term memory classification. Concurrency and Computation: Practice and Experience, Wiley Online Library 34, e7250 (2022)
Garg, A., Nigam, S., Singh, R.: Vision based Human Activity Recognition using Hybrid Deep Learning. CSI, IEEE, 1–6 (2022)
Singh, R., Nigam, S., Singh, A.K., Elhoseny, M.: Wavelets for Activity Recognition. Intelligent Wavelet Based Techniques for Advanced Multimedia Applications, Springer 10, 109–121 (2020)
Dwivedi, N., Singh, D.K., Kushwaha, D.S.: A Novel Approach for Suspicious Activity Detection with Deep Learning. Multimedia Tools and Applications, pp. 1–24. Springer (2023)
Badhagouni, S.K., ViswanadhaRaju, S.: HBA optimized Efficient CNN in Human Activity Recognition. The Imaging Science Journal, Taylor & Francis 71, 66–81 (2023)
Saif, A.S., Wollega, E.D., Kalevela, S.A.: Spatio-temporal features based human action recognition using convolutional long short-term deep neural network. Int. J. Adv. Comp. Sci. Appl. Sci. Info. (SAI) Organization Limited 14, 66–81 (2023)
Schuldt, C., Laptev, I., Caputo, B.: Recognizing Human Actions: A Local SVM Approach. ICPR, IEEE 3, 32–36 (2004)
Blank, M., Gorelick, L., Shechtman, E., Irani, M., Basri, R.: Actions as Space-time Shapes. ICCV, IEEE 2, 1395–1402 (2005)
Nigam, S., Khare, A.: Integration of moment invariants and uniform local binary patterns for human activity recognition in video sequences. Multimedia Tools and Applications, Springer 75, 17303–17332 (2016)
Naveed, H., Khan, G.A.U., Siddiqi, A., Khan, M.U.G.: Human activity recognition using mixture of heterogeneous features and sequential minimal optimization. International Journal of Machine Learning and Cybernetics, Springer 10, 2329–2340 (2019)
Nadeem, A., Jalal, A., Kim, K.: Human Actions Tracking and Recognition based on Body Parts Detection via Artificial Neural Network. ICACS, IEEE, pp. 1–6 (2020)
Song, B.: Application of Fuzzy Clustering Model in the Classification of Sports Training Movements. Computational Intelligence and Neuroscience, Hindawi, 2022 (2022)
Haq, I.U., Iwata, T., Kawahara, Y.: Dynamic mode decomposition via convolutional autoencoders for dynamics modeling in videos. Comput. Vis. Image Underst. 216, 103355 (2022)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Jain, S., Garg, A., Nigam, S., Singh, R., Shastri, A., Singh, I. (2024). Convolutional Autoencoder for Vision-Based Human Activity Recognition. In: Choi, B.J., Singh, D., Tiwary, U.S., Chung, WY. (eds) Intelligent Human Computer Interaction. IHCI 2023. Lecture Notes in Computer Science, vol 14532. Springer, Cham. https://doi.org/10.1007/978-3-031-53830-8_10
Download citation
DOI: https://doi.org/10.1007/978-3-031-53830-8_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-53829-2
Online ISBN: 978-3-031-53830-8
eBook Packages: Computer ScienceComputer Science (R0)