Advancements in Human Activity Recognition: Novel Shape Index Local Ternary Pattern and Hybrid Classifier Model for Enhanced Video Data Analysis
Keywords:
Artificial Neural Network, Human activity recognition, Hybrid Classifier, k-Nearest Neighbour classifiers, Shape Index Local Ternary Pattern, Support Vector MachineAbstract
Human activity recognition using video data is a fundamental yet challenging task in computer vision. This paper proposes a novel method for spatial feature extraction and classification model design to enhance the accuracy and robustness of human activity recognition systems. The primary proposed work is the development of the Shape Index Local Ternary Pattern, a novel spatial feature extraction method aimed at capturing intricate spatial relationships within video data. This feature significantly enhances the representation of spatial information, thereby overcoming the limitations of traditional texture-based features. Moreover, a Hybrid Classifier Model is proposed, comprising an initial layer integrating a Support Vector Machine and k-Nearest Neighbour classifiers. The outputs of these classifiers are fed into an Artificial Neural Network for the final classification. This hybrid approach combines the strengths of different classifiers and artificial neural network architectures, resulting in improved classification accuracy and robustness. The efficacy of the proposed method was rigorously evaluated and validated through extensive experiments conducted on UCF101 and HACS datasets. The results showed superior performance in accurately identifying a range of human activities. The Shape Index Local Ternary Pattern feature demonstrates its effectiveness in capturing intricate spatial details, whereas the hybrid model shows substantial advancements in classification accuracy and robustness.
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H. Mliki, F. Bouhlel, and M. Hammami, "Human activity recognition from UAV- captured video sequences," Pattern Recognit., vol. 100, p. 107140, 2020, doi: https://doi.org/10.1016/j.patcog.2019.107140.
Z. A. Khan and W. Sohn, "Abnormal human activity recognition system based on R-transform and kernel discriminant technique for elderly home care," IEEE Trans. Consum. Electron., vol. 57, no. 4, pp. 1843–1850, 2011, doi: 10.1109/TCE.2011.6131162.
Y. Chen, L. Yu, K. Ota, and M. Dong, "Robust activity recognition for aging society," IEEE J. Biomed. Heal. Informatics, vol. 22, no. 6, pp. 1754–1764, 2018, doi: 10.1109/JBHI.2018.2819182.
K. Viard, M. P. Fanti, G. Faraut, and J.-J. Lesage, "Human Activity Discovery and Recognition Using Probabilistic Finite-State Automata," IEEE Trans. Autom. Sci. Eng., vol. 17, no. 4, pp. 2085–2096, 2020, doi: 10.1109/TASE.2020.2989226.
J. Ye, G. J. Qi, N. Zhuang, H. Hu, and K. A. Hua, "Learning Compact Features for Human Activity Recognition Via Probabilistic First-Take-All," IEEE Trans. Pattern Anal. Mach. Intell., vol. 42, no. 1, pp. 126–139, 2020, doi: 10.1109/TPAMI.2018.2874455.
J. Luo, W. Wang, and H. Qi, "Spatio-temporal feature extraction and representation for RGB-D human action recognition," Pattern Recognit. Lett., vol. 50, pp. 139–148, 2014, doi: https://doi.org/10.1016/j.patrec.2014.03.024.
V. Kellokumpu, G. Zhao, and M. Pietikäinen, "Human Activity Recognition Using a Dynamic Texture Based Method," 2008. [Online]. Available: https://api.semanticscholar.org/CorpusID:1280572
S. Rahman, J. See, and C. C. Ho, "Exploiting textures for better action recognition in low-quality videos," EURASIP J. Image Video Process., vol. 2017, no. 1, p. 74, 2017, doi: 10.1186/s13640-017-0221-2.
S. Abbaspour, F. Fotouhi, A. Sedaghatbaf, H. Fotouhi, M. Vahabi, and M. Lindén, "A Comparative Analysis of Hybrid Deep Learning Models for Human Activity Recognition," Sensors, vol. 20, 2020, doi: 10.3390/s20195707.
M. Arshad et al., "Hybrid Machine Learning Techniques to detect Real-Time Human Activity using UCI Dataset," Int. J. Sci. Eng. Res., vol. 4, p. 6, 2021.
D. K. Vishwakarma and R. Kapoor, "Hybrid classifier based human activity recognition using the silhouette and cells," Expert Syst. Appl., vol. 42, no. 20, pp. 6957–6965, 2015, doi: https://doi.org/10.1016/j.eswa.2015.04.039.
R. R. Subramanian and V. Vasudevan, "A deep genetic algorithm for human activity recognition leveraging fog computing frameworks," J. Vis. Commun. Image Represent., vol. 77, p. 103132, 2021, doi: https://doi.org/10.1016/j.jvcir.2021.103132.
M. M. H. Shuvo, N. Ahmed, K. Nouduri, and K. Palaniappan, "A Hybrid Approach for Human Activity Recognition with Support Vector Machine and 1D Convolutional Neural Network," 2020. doi: 10.1109/AIPR50011.2020.9425332.
T. R. Mim et al., "GRU-INC: An inception-attention based approach using GRU for human activity recognition," Expert Syst. Appl., vol. 216, p. 119419, 2023, doi: https://doi.org/10.1016/j.eswa.2022.119419.
X. Yin, Z. Liu, D. Liu, and X. Ren, "A Novel CNN-based Bi-LSTM parallel model with attention mechanism for human activity recognition with noisy data," Sci. Rep., vol. 12, no. 1, pp. 1–11, 2022, doi: 10.1038/s41598-022-11880-8.
R. J. Nemati and M. Y. Javed, "Fingerprint verification using filter-bank of Gabor and Log Gabor filters," in 2008 15th International Conference on Systems, Signals and Image Processing, 2008, pp. 363–366. doi: 10.1109/IWSSIP.2008.4604442.
J. Luo, "Feature Extraction and Recognition for Human Action Recognition," University of Tennessee, Knoxville, 2014.
P. Febin, K. Jayasree, and P. T. Joy, "Violence detection in videos for an intelligent surveillance system using MoBSIFT and movement filtering algorithm," Pattern Anal. Appl., vol. 23, no. 2, pp. 611–623, 2020, doi: 10.1007/s10044-019-00821-3.
S. Megrhi, W. Mseddi, and A. Beghdadi, "Spatio-temporal SURF for Human Action Recognition," 2013. doi: 10.1007/978-3-319-03731-8_47.
M. Niu, X. Mao, J. Liang, and B. Niu, "Object Tracking Based on Extended SURF and Particle Filter," in Intelligent Computing Theories and Technology, 2013, pp. 649–657.
J. J. Koenderink and A. J. van Doorn, "Surface shape and curvature scales," Image Vis. Comput., vol. 10, no. 8, pp. 557–564, 1992, doi: https://doi.org/10.1016/0262-8856(92)90076-F.
N. Alpaslan and K. Hanbay, "Multi-Scale Shape Index-Based Local Binary Patterns for Texture Classification," IEEE Signal Process. Lett., vol. 27, pp. 660–664, 2020, doi: 10.1109/LSP.2020.2987474.
K. Soomro, A. R. Zamir, and M. Shah, "UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild," no. November, 2012, [Online]. Available: http://arxiv.org/abs/1212.0402
H. Zhao, Z. Yan, L. Torresani, and A. Torralba, "{HACS}: Human Action Clips and Segments Dataset for Recognition and Temporal Localization," arXiv Prepr. arXiv1712. 09374, 2019.
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