Abstract
Video surveillance cameras have been around for ages. Taking a step forward, a system is built that does the work of a surveillance camera and helps us understand a child’s behavioral and emotional aspects. The research proposes a solution that aims to help the working parents of children between the ages of 4 and 12. Due to constant work commitments, some parents are forced to leave their children at home alone or with a caretaker. The objective of the research is to detect and recognize the day-to-day activities a child performs using the human activity recognition model. Emotions play relevant roles in social and daily life so after detecting the activity being performed, the aim is to detect the emotion expressed by the child with the help of emotional analysis using the facial expression recognition model. The model also analyzes the data recorded in the system and does the graphical analysis of the emotions expressed by the child. The research also includes a model to keep a check on the behavioral aspects of the caretaker/guardian present at home to prevent inappropriate behavior toward the child and also to protect the child from being a victim of child abuse or careless handling of harmful objects. The research also provides the dataset for child activity recognition and child abuse detection that can animate researchers interested in activity recognition and abuse detection for children. Random forest yields an accuracy of 91.27% for activity recognition which is higher than the other experimented model. The proposed AbuseNet is superior to other ImageNet models with 98.20% accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zhang, Y.: Support vector machine classification algorithm and its application. In: International Conference on Information Computing and Applications, pp. 179–186. Springer, Berlin, Heidelberg (2012)
Oshiro, T.M., Perez, P.S., Baranauskas, J.A.: How many trees in a random forest? In: International workshop on machine learning and data mining in pattern recognition, pp. 154–168. Springer, Berlin, Heidelberg (2012)
Thomas, S., Ganapathy, S., Saon, G., Soltau, H.: Analyzing convolutional neural networks for speech activity detection in mismatched acoustic conditions. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2519–2523. IEEE (2014)
Koutník, J., Schmidhuber, J., Gomez, F.: Evolving deep unsupervised convolutional networks for vision-based reinforcement learning. In: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation, pp. 541–548 (2014)
Wang, C., Wang, Y., Yuille, A.L.: An approach to pose-based action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 915–922 (2013)
Cheng, K., Zhang, Y., He, X., Chen, W., Cheng, J., Lu, H.: Skeleton-based action recognition with shift graph convolutional network. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 183–192 (2020)
Bevilacqua, A., MacDonald, K., Rangarej, A., Widjaya, V., Caulfield, B., Kechadi, T.: Human activity recognition with convolutional neural networks. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 541–552. Springer, Cham (2018)
Alsheikh, M.A., Selim, A., Niyato, D., Doyle, L., Lin, S., Tan, H.P.: Deep activity recognition models with triaxial accelerometers. arXiv preprint arXiv:1511.04664 (2015)
Wang, J., Liu, Z., Wu, Y., Yuan, J.: Mining actionlet ensemble for action recognition with depth cameras. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1290–1297. IEEE (2012)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3431–3440 (2015)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Proc. Syst. 25, 1097–1105 (2012)
Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448–456. PMLR (2015)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1), 1929–1958 (2014)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)
Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251–1258 (2017)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, pp 315–323. JMLR Workshop and Conference Proceedings (2011)
Goodfellow, I.J., Erhan, D., Carrier, P.L., Courville, A., Mirza, M., Hamner, B., Cukierski, W., Tang, Y., Thaler, D., Lee, D.H., Zhou, Y.: Challenges in representation learning: A report on three machine learning contests. In: International Conference on Neural Information Processing, pp. 117–124. Springer, Berlin, Heidelberg (2013)
Nehete, J.O., Agrawal, D.G.: Real time Recognition and monitoring a child activity based on smart embedded sensor fusion and GSM technology
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Khedkar, S., Naik, A., Mane, O., Gurnani, A., Amesur, K. (2022). Kids View—A Parents Companion. In: Roy, S., Sinwar, D., Perumal, T., Slowik, A., Tavares, J.M.R.S. (eds) Innovations in Computational Intelligence and Computer Vision . Advances in Intelligent Systems and Computing, vol 1424. Springer, Singapore. https://doi.org/10.1007/978-981-19-0475-2_16
Download citation
DOI: https://doi.org/10.1007/978-981-19-0475-2_16
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-0474-5
Online ISBN: 978-981-19-0475-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)