Project Zeus : video based behavioural modelling of non-linear transportation system for improved planning &urban construction projects

• Autorzy: Kannan J. R. , Rai A. , Szpytko J. , Pandya Y. C.

Identyfikatory

DOI

10.5604/01.3001.0010.3152

• Języki publikacji: EN

Abstrakty

EN

The ability to analyse the traffic and urban mobility pattern with the help of video analytics, which occur in massive volumes of surveillance video will lead us to provide a knowledge based for the urban planners and policy makers to come up with better construction planning. This will soothe the needs of urban commuters and thereby saving unnecessary spillage of money on the construction projects. In this research project, we present an artificial intelligence framework for transport video analytic; which autonomously models behavioural patterns of commuters and flow of traffic, wherein it taxonomically classifies essential patterns based on geometrical feature points of interest to facilitate reality mining. This behavioural pattern of commuter and traffic flow can later be queried and fetched through the newly mathematically programmed ontological data warehousing module, where such reality mined contextual data could be used for sharing essential data.

Słowa kluczowe

EN

algorithm; computer vision; traffic activity recognition; event detection; activity analysis; behaviour modelling

• Wydawca: Łukasiewicz Research Network - Institute of Aviation ; European Science Society of Powertrain and Transport KONES Poland ; Wydawnictwo Instytutu Technicznego Wojsk Lotniczych
• Czasopismo: Journal of KONES
• Rocznik: 2017
• Tom: Vol. 24, No. 4
• Strony: 71--81
• Opis fizyczny: Bibliogr. 10 poz., rys.

Twórcy

autor

Kannan J. R.

• VIT University School of Computing Science & Engineering, India

autor

Rai A.

• VIT University School of Computing Science & Engineering, India

autor

Szpytko J.

• AGH University of Science and Technology, Poland

autor

Pandya Y. C.

• AGH University of Science and Technology, Poland

Bibliografia

• [1] Aggarwal, J., Ryoo, M., Human activity analysis: A survey - ACM Computing Surveys 43, 1-43, 2011.
• [2] Aggarwal, M. S. R. J. K., Semantic representation and recognition of continued and recursive human activities, International Journal of Computer, Vision 82, 1-24, 2009.
• [3] Blank, M., Gorelick, L., Shechtman, E., Irani, M., Basri, R., Actions as space-time shapes, IEEE International Conference on Computer Vision (ICCV), pp. 1395-1402, 2005.
• [4] Blei, D., Lafferty, J., Correlated Topic Models, Advances in neural information processing systems, 18, 147, 2006.
• [5] Blei, D. M., Ng, A. Y., Jordan, M. I., Latent Dirichlet allocation, Journal of Machine Learning Research, 3, 993-1022, 2003.
• [6] Rai, A., Artificial Intelligence for Emotion Recognition, Journal of Artificial Intelligence Research & Advances, 1(2), 24-30, 2014.
• [7] Rai, A., Sakkaravarthi Ramanathan, Kannan, R. J., Quasi Opportunistic Supercomputing for Geospatial Socially Networked Mobile Devices, Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE), IEEE 25th International Conference, 2016.
• [8] Rai, A., Multispectral Image Denoising using Bi-Directional Recurrent Neural Network with DPCA Algorithm, Journal of Image Processing & Pattern Recognition Progress, 2(1), 25-30, 2015.
• [9] Rai, A., Attribute Based Level Adaptive Thresholding Algorithm for Object Extraction, Journal of Advancements in Robotics, 1(1), 29-33, 2015.
• [10] Rai, A., Artificial Intelligence for Emotion Recognition, Journal of Artificial Intelligence Research & Advances, 1(2), 24-30, 2014.

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).