Towards Real-Time Human Behavior Understanding: A Suboptimal Shape Descriptor

Year 2022, , 769 - 777, 31.08.2022

Güray Sonugur , Elif Ebru Çakı , Simge Ayşe Akan , Celal Onur Gökçe

https://doi.org/10.35414/akufemubid.1099907

Cited By: 1

Abstract

In this study, two novel shape descriptors are proposed to be used in human behavior understanding problem. First is optimal shape descriptor, which has high performance but works very slow due to high algorithmic complexity. Second is suboptimal shape descriptor, performance of which is very close to optimal one, but works much more faster. Optimal means using minimum data to represent maximum knowledge. Algorithms are run on Weizmann dataset and results are shown both as figure and video link. Classification was performed using 12 statistical features extracted from the data sets' human silhouettes. An accuracy rating of 92 percent was obtained by using Euclidean distance in classification.

Keywords

Human behavior understanding, Image processing, Suboptima method, Weizmann

Gerçek Zamanlı İnsan Davranışı Anlamaya Doğru: Optimal-Altı Bir Şekil Tanımlayıcı

Abstract

Bu çalışmada insan davranışı anlama (İDA) probleminin çözümünde kullanılmak üzere özgün optimal ve optimal-altı şekil tanımlayıcıları önerilmiştir. Bu şekilde en az veri kullanımıyla en fazla davranış bilgisini sınıflandırabilmek amaçlanmıştır. Optimal şekil tanımlayıcısı başarısı yüksek olmakla beraber algoritmik karmaşıklığı yüksek olduğu için oldukça yavaş çalışmaktadır. Bu sorunu gidermek için daha hızlı çalışan bir optimal-altı tanımlayıcı önerilmiştir. Optimal-altı tanımlayıcının başarısı optimal tanımlayıcıya çok yakın olmakla beraber çok daha düşük algoritmik karmaşıklığa sahip olup çok daha hızlıdır. Sonuçlar Weizmann veri setinde denenmiş ve şekiller ve video bağlantıları ile gösterilmiştir. Veri setinden elde edilen siluet görüntü akışlarından 12 adet istatistiksel öznitelik çıkarılıp sınıflandırmada kullanılmıştır. Sınıflandırmada kullanılan Öklid uzaklığı yöntemi sayesinde oldukça hızlı sonuçlar üretilerek %92 doğruluk oranına ulaşılmıştır.

Keywords

İnsan davranışı anlama, Görüntü işleme, Optimal-altı yöntem, Weizmann

References

• Acampora, G., Foggia, P., Saggese, A., Vento, M. 2015. A hierarchical neuro-fuzzy architecture for human behavior analysis, Information Sciences, 310, 130-148.
• Acharya, B. R. ve Gantayat, P. K. 2015. Recognition of human unusual activity in surveillance videos. International Journal of Research and Scientific Innovation (IJRSI), 2(5), 18-23.
• Acharjya, P. P., Das, R., & Ghoshal, D. 2012. Study and comparison of different edge detectors for image segmentation. Global Journal of Computer Science and Technology.
• Akdağ E. 2015. Human Behavior Understanding Through 3D Data, Yüksek Lisans Tezi, Fen Bilimleri Enstitüsü, ODTÜ, Ankara, 87.
• Akilan, T., Wu, Q. J., Yang, Y. 2018. Fusion-based foreground enhancement for background subtraction using multivariate multi-model Gaussian distribution. Information Sciences, 430, 414-431.
• Antonakaki, P., Kosmopoulos, D., & Perantonis, S. J. 2011. Detecting abnormal human behaviour using multiple cameras. Signal Processing, 89(9), 1723-1738.
• Aslan, M., Sengur, A. , Xiao, Y., Wang, H., Ince, M.C., Ma, X. 2015. Shape feature encoding via fisher vector for efficient fall detection in depth-videos, Applied Soft Computing.
• Avlash, M., & Kaur, L. 2013. Performances analysis of different edge detection methods on road images. International Journal on Recent and Innovation Trends in Computing and Communication, 2(6), 27-38.
• Blank M., Gorelick L., Shechtman E., Irani M. ve Basri R. 2005. Actions as Space-Time Shapes, The Tenth IEEE International Conference on Computer Vision (ICCV), Beiging, China, 1395-1402.
• Chianese, A., Moscato, V., ve Picariello, A. 2008. Detecting abnormal activities in video sequences. In Proceedings of the 2008 Ambi-Sys workshop on Ambient media delivery and interactive television, 1-8.
• De Campos, T. 2014. A survey on computer vision tools for action recognition, crowd surveillance and suspect retrieval, XXXIV congresso da sociedade brasileira de computacao (CSBC) 1123-1132.
• Dhulekar, P., Gandhe, S. T., Chitte, H., ve Pardeshi, K. 2017. Human action recognition: An overview. In Proceedings of the international conference on data engineering and communication technology, Springer, Singapore, 481-488.
• Feng, Y., Yuan, Y. ve Lu, X. 2017. Learning deep event models for crowd anomaly detection. Neurocomputing, 219, 548-556. Gökçe C.O., 2016. Human Behavior Understanding Using Video Analysis, Doktora Tezi, Fen Bilimleri Enstitüsü, ODTÜ, Ankara, 106.
• Gökçe, B., ve Sonugür, G. 2022. Recognition of dynamic objects from UGVs using Interconnected Neural network-based Computer Vision System. Automatika, 63(2), 244-258.
• Jain, A., Gupta, M., & Tazi, S. N. 2014. Comparison of edge detectors. In 2014 International Conference on Medical Imaging, m-Health and Emerging Communication Systems (MedCom), 289-294. IEEE.
• Johnson, N., ve Hogg, D. 1996. Learning the distribution of object trajectories for event recognition. Image and Vision computing, 14(8), 609-615.
• Mabrouk, A. B., ve Zagrouba, E. 2018. Abnormal behavior recognition for intelligent video surveillance systems: A review. Expert Systems with Applications, 91, 480-491.
• Oluwatoyin, P.P. ve Kejun, W. 2012. Video-based abnormal human behavior recognition – a review, IEEE Transactions on Systems, Man, and Cybernetics, Part C, 42 (6). 865-878.
• Park, K., Lin, Y., Metsis, V., Le, Z., ve Makedon, F. 2010, June. Abnormal human behavioral pattern detection in assisted living environments. In Proceedings of the 3rd International Conference on Pervasive Technologies Related to Assistive Environments, 1-8.
• Siddharth, R. ve Anupam, A., 2015. Vision based hand gesture recognition for human computer interaction: A survey, Artificial Intelligence Review, 43(1).
• Sakpal, N. S. ve Sabnis, M., 2018. Adaptive background subtraction in images. In 2018 International Conference on Advances in Communication and Computing Technology (ICACCT), 439-444. IEEE.
• Wang, L. ve Maybank, S., 2004. A survey on visual surveillance of object motion and behaviors. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 34(3), 334-352.
• Weiya, R. L. Guohui, S., Boliang ve Kuihua, H. 2015. Unsupervised kernel learning for abnormal events detection, The Visual Computer, 31, 245-255, 10.1007/s00371-013-0915-0.
• Xu, L., Gong, C., Yang, J., Wu, Q., & Yao, L., 2014. Violent video detection based on MoSIFT feature and sparse coding. In 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 3538-3542. IEEE.
• Yang, M. H. ve Ahuja, N. 1998. Extraction and classification of visual motion patterns for hand gesture recognition. In Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No. 98CB36231) 892-897. IEEE.
• Yogameena, B., ve Priya, K. S., 2015. Synoptic video based human crowd behavior analysis for forensic video surveillance. In 2015 Eighth International Conference on Advances in Pattern Recognition (ICAPR), 1-6. IEEE.
• Zhang, T., Jia, W., Yang, B., Yang, J., He, X., ve Zheng, Z. 2017. MoWLD: a robust motion image descriptor for violence detection. Multimedia Tools and Applications, 76(1), 1419-1438.
• Zhao, F. ve Li, J., 2014. Pedestrian motion tracking and crowd abnormal behavior detection based on intelligent video surveillance. Journal of Networks, 9(10), 2598.
• Zhao, Y. ve Su, Y. 2017. Vehicles detection in complex urban scenes using Gaussian mixture model with FMCW radar. IEEE Sensors Journal, 17(18), 5948-5953.
• Zhou, S., Shen, W., Zeng, D., Fang, M., Wei, Y. ve Zhang, Z., 2016. Spatial–temporal convolutional neural networks for anomaly detection and localization in crowded scenes. Signal Processing: Image Communication, 47, 358-368.