Behavior Detection by Trajectory Analyzing Using Topic Modeling
Publish place: International Journal of Mechatronics, Electrical and Computer Technology، Vol: 4، Issue: 12
Publish Year: 1393
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_IJMEC-4-12_027
تاریخ نمایه سازی: 16 فروردین 1395
Abstract:
In this paper, an unsupervised framework is presented to learn motion patterns by using hierarchical Bayesian models. It is also employed for activity analysis in visual surveillance. In this research, the concept of activities as motion patterns is considered as a correspondence to far-field camera view. Objects are tracked by using low-level features and then the location and speed of objects are computed as a feature along with trajectories. Under LDA probabilistic model, activities’ distributions are learned in feature space. Since there is not an analytic solution for these models, variational inference method is used to approximate latent parameters of the model. This approach is separately measured on the captured data of several cameras and acceptable results are obtained
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Authors
Mojtaba Gholipour
Computer Engineering Department, Faculty of Engineering, Islamic Azad University Sari Branch, Sari, Iran.
Ali Aghagolzadeh
Faculty of Electrical and Computer Engineering, Babol University of Technology, Babol, Iran.
Javad Vahidi
Iran University of Science and Technology, Information Technology Faculty, Behshahr, Iran.