DEEP LEARNING HUMAN ACTIVITY RECOGNITION

Miss. Kasturi Harbade; Mr. Lowlesh Yadav; Mr. Ashish Deharkar

doi:10.59367/fgrekh60

Authors

Miss. Kasturi Harbade Department of Computer Science and Engineering, Technology, Shri Sai, College Chandrapur, India
Mr. Lowlesh Yadav Department of Computer Science and Engineering, Technology, Shri Sai, College Chandrapur, India
Mr. Ashish Deharkar Department of Computer Science and Engineering, Technology, Shri Sai, College Chandrapur, India

DOI:

https://doi.org/10.59367/fgrekh60

Keywords:

Deep Learning, Human Activity Recognition, Detection

Abstract

Human activity recognition is an area of interest in colorful disciplines similar as senior and health care, smart- structures and eavesdrop shaft, with multiple approaches to working the problem directly and efficiently. For numerous times hand- drafted features were manually uprooted from raw data signals, and conditioning were classified using support vector machines and hidden Markov models. To further ameliorate on this system and to prize applicable features in an automated fashion, deep literacy styles have been used. The most common of these styles are Long Short- Term Memory models (LSTM), which can take the successional nature of the data into consideration and outperform being ways, but which have two main risks; longer training times and loss of distant pass memory. A relevantly new type of network, the Temporal Convolutional Network (TCN), overcomes these risks, as it takes significantly lower time to train than LSTMs and also has a lesser capability to capture further of the long-term dependences than LSTMs. When paired with a Convolutional Auto- Encoder (CAE) to remove noise and reduce the complexity of the problem, our results show that both models perform inversely well, the results also show, for assiduity operations, the TCN can directly be used for fall discovery or analogous events within a smart structure terrain.

References

Abdelnasser, H., Youssef, M., Harras, K.A.: Wigest: A ubiquitous wifi-based gesture recognition system. In: IEEE INFOCOM. pp. 1472–1480 (2015)

Bai, S., Kolter, J.Z., Koltun, V.: An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. CoRR abs/1803.01271 (2018)

Cippitelli, E., Fioranelli, F., Gambi, E., Spinsante, S.: Radar and rgb-depth sensors for fall detection: A review. IEEE Sensors Journal 17(12), 3585–3604 (2017)

Doll´ar, P., Rabaud, V., Cottrell, G., Belongie, S.: Behavior recognition via sparse spatio-temporal features. VS-PETS Beijing, China (2005)

Halperin, D., Hu, W., Sheth, A., Wetherall, D.: Tool release: Gathering 802.11 n traces with channel state information. ACM SIGCOMM Computer Communication Review 41(1), 53–53 (2011)

Hou, J., Wang, G., Chen, X., Xue, J.H., Zhu, R., Yang, H.: Spatial-temporal attention res-tcn for skeleton-based dynamic hand gesture recognition. In: Proc. of the ECCV. pp. 0–0 (2018)

Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., Fei-Fei, L.: Largescale video classification with convolutional neural networks. In: Proc. of the IEEE conference on CVPR. pp. 1725–1732 (2014)

Laptev, I.: On space-time interest points. IJCV 64(2-3), 107–123 (2005)

Lea, C., Flynn, M.D., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks for action segmentation and detection. In: Proc. of the IEEE Conference on CVPR. pp. 156–165 (2017)

LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. nature 521(7553), 436 (2015)

Li, H., Ota, K., Dong, M., Guo, M.: Learning human activities through wi-fi channel state information with multiple access points. IEEE Com Mag 56(5) (2018)

Morales, J., Akopian, D.: Physical activity recognition by smartphones, a survey. Biocybernetics and Biomedical Engineering 37(3), 388–400 (2017)

Nair, N., Thomas, C., Jayagopi, D.B.: Human activity recognition using temporal convolutional network. In: Proc. of the 5th iWOAR. p. 17 (2018)

Pu, Q., Gupta, S., Gollakota, S., Patel, S.: Whole-home gesture recognition using wireless signals. In: Proc. of the 19th ACM MobiCom. pp. 27–38 (2013)

Qolomany, B., Al-Fuqaha, A., Benhaddou, D., Gupta, A.: Role of deep lstm neural networks and wi-fi networks in support of occupancy prediction in smart buildings. In: IEEE 19th Int. Conf. on HPCC; IEEE 15th Int. Conf. on SmartCity; IEEE 3rd Int. Conf. on DSS. pp. 50–57 (2017)

Ronao, C.A., Cho, S.B.: Human activity recognition with smartphone sensors using deep learning neural networks. Expert systems with apls 59, 235–244 (2016)

Tr˘asc˘au, M., Nan, M., Florea, A.M.: Spatio-temporal features in action recognition using 3d skeletal joints. Sensors 19(2), 423 (2019)

Wang, F., Gong, W., Liu, J., Wu, K.: Channel selective activity recognition with wifi: A deep learning approach exploring wideband information. IEEE Transactions on Network Science and Engineering (2018)

Wang, Y., Wu, K., Ni, L.M.: Wifall: Device-free fall detection by wireless networks. IEEE Transactions on Mobile Computing 16(2), 581–594 (2016)

Yatani, K., Truong, K.N.: Bodyscope: a wearable acoustic sensor for activity recognition. In: Proc. of ACM Conference on Ubiquitous Computing. pp. 341–350 (2012)

Yousefi, S., Narui, H., Dayal, S., Ermon, S., Valaee, S.: A survey on behavior recognition using wifi channel state information. IEEE Comms Mag. 55(10) (2017)

Yue-Hei Ng, J., Hausknecht, M., Vijayanarasimhan, S., Vinyals, O., Monga, R., Toderici, G.: Beyond short snippets: Deep networks for video classification. In: Proc. of the IEEE conference on CVPR. pp. 4694–4702 (2015)

Zou, H., Zhou, Y., Yang, J., Spanos, C.J.: Towards occupant activity driven smart buildings via wifi-enabled iot devices and deep learning. Energy and Buildings 177, 12–22 (2018)

Narendra Ahuja and Sinisa Todorovic. Learning the taxonomy and models of categories present in arbitrary images. In International Conference on Computer Vision, 2007.

Yoshua Bengio. Learning deep architectures for ai. Foundations and Trends R in Machine Learning, 2(1):1–127, 2009.

Dan Ciresan, Alessandro Giusti, Juergen Schmidhuber, et al. Deep neural networks segment neuronal membranes in electron microscopy images. In Advances in Neural Information Processing Systems 25, 2012. [4] Navneet Dalal and Bill Triggs. Histograms of oriented gradients for human detection. In Computer Vision and Pattern Recognition, 2005.

J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. ImageNet: A Large-Scale Hierarchical Image Database. In Computer Vision and Pattern Recognition, 2009.

John Duchi, Elad Hazan, and Yoram Singer. Adaptive subgradient methods for online learning and stochastic optimization. In Conference on Learning Theory. ACL, 2010.

M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The pascal visual object classes (voc) challenge. International Journal of Computer Vision, 88(2):303–338, 2010.

Clement Farabet, Camille Couprie, Laurent Najman, and Yann LeCun. Learning hierarchical features ´ for scene labeling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8):1915–1929, 2013. [9] Pedro F Felzenszwalb, Ross B Girshick, David McAllester, and Deva Ramanan. Object detection with discriminatively trained part-based models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(9):1627–1645, 2010.

Sanja Fidler and Ales Leonardis. Towards scalable representations of object categories: Learning a hier- ˇ archy of parts. In Computer Vision and Pattern Recognition, 2007.

R. B. Girshick, P. F. Felzenszwalb, and D. McAllester. Discriminatively trained deformable part models, release 5. http://people.cs.uchicago.edu/ rbg/latent-release5/.

Geoffrey E Hinton and Ruslan R Salakhutdinov. Reducing the dimensionality of data with neural networks. Science, 313(5786):504–507, 2006.

Iasonas Kokkinos and Alan Yuille. Inference and learning with hierarchical shape models. International Journal of Computer Vision, 93(2):201–225, 2011.

Lowlesh Nandkishor Yadav,”Predictive Acknowdgement using TRE System to reduce cost and bandwidth” IJRECE VOL. 7ISSUE 1 (JANUARY- MARCH 2019 ) pg no 275-278