Spatio-temporal segmentation for video surveillance

2001 ◽  
Vol 37 (1) ◽  
pp. 20 ◽  
Author(s):  
Hongzan Sun ◽  
Tieniu Tan
Keyword(s):  
Video Surveillance ◽  
Temporal Segmentation ◽  
Spatio Temporal

scholarly journals Spatio-temporal Segmentation Based Adaptive Compression of Dynamic Mesh Sequences

2020 ◽  
Vol 16 (1) ◽  
pp. 1-24
Author(s):  
Guoliang Luo ◽  
Zhigang Deng ◽  
Xin Zhao ◽  
Xiaogang Jin ◽  
Wei Zeng ◽  
...  
Keyword(s):  
Dynamic Mesh ◽  
Temporal Segmentation ◽  
Adaptive Compression ◽  
Spatio Temporal

scholarly journals Spatio-Temporal Visual Analysis for Urban Traffic Characters Based on Video Surveillance Camera Data

2021 ◽  
Vol 10 (3) ◽  
pp. 177
Author(s):  
Haochen Zou ◽  
Keyan Cao ◽  
Chong Jiang
Keyword(s):  
Video Surveillance ◽  
Traffic Management ◽  
Visual Analysis ◽  
Urban Traffic ◽  
License Plate ◽  
Road Vehicle ◽  
Urban Road ◽  
Surveillance Camera ◽  
Vehicle Density ◽  
Spatio Temporal

Urban road traffic spatio-temporal characters reflect how citizens move and how goods are transported, which is crucial for trip planning, traffic management, and urban design. Video surveillance camera plays an important role in intelligent transport systems (ITS) for recognizing license plate numbers. This paper proposes a spatio-temporal visualization method to discover urban road vehicle density, city-wide regional vehicle density, and hot routes using license plate number data recorded by video surveillance cameras. To improve the accuracy of the visualization effect, during data analysis and processing, this paper utilized Internet crawler technology and adopted an outlier detection algorithm based on the Dixon detection method. In the design of the visualization map, this paper established an urban road vehicle traffic index to intuitively and quantitatively reveal the traffic operation situation of the area. To verify the feasibility of the method, an experiment in Guiyang on data from road video surveillance camera system was conducted. Multiple urban traffic spatial and temporal characters are recognized concisely and efficiently from three visualization maps. The results show the satisfactory performance of the proposed framework in terms of visual analysis, which will facilitate traffic management and operation.

scholarly journals MULTIVARIATE MATHEMATICAL MORPHOLOGY FOR DCE-MRI IMAGE ANALYSIS IN ANGIOGENESIS STUDIES

2014 ◽  
Vol 34 (1) ◽  
pp. 1 ◽  
Author(s):  
Guillaume Noyel ◽  
Jesus Angulo ◽  
Dominique Jeulin ◽  
Daniel Balvay ◽  
Charles-André Cuenod
Keyword(s):  
Noise Reduction ◽  
Data Sets ◽  
Computer Aided Detection ◽  
Segmentation Method ◽  
Temporal Segmentation ◽  
Dce Mri ◽  
Contrast Enhanced ◽  
Key Points ◽  
Multivariate Images ◽  
Spatio Temporal

We propose a new computer aided detection framework for tumours acquired on DCE-MRI (Dynamic Contrast Enhanced Magnetic Resonance Imaging) series on small animals. To perform this approach, we consider DCE-MRI series as multivariate images. A full multivariate segmentation method based on dimensionality reduction, noise filtering, supervised classification and stochastic watershed is explained and tested on several data sets. The two main key-points introduced in this paper are noise reduction preserving contours and spatio temporal segmentation by stochastic watershed. Noise reduction is performed in a special way to select factorial axes of Factor Correspondence Analysis in order to preserves contours. Then a spatio-temporal approach based on stochastic watershed is used to segment tumours. The results obtained are in accordance with the diagnosis of the medical doctors.

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