A Transform Domain Approach to Real-Time Foreground Segmentation in Video Sequences

Author(s):  
Juhua Zhu ◽  
S.C. Schwartz ◽  
Bede Liu
2021 ◽  
pp. 1-15
Author(s):  
V. Muhammed Anees ◽  
G. Santhosh Kumar

Crowd behaviour analysis and management have become a significant research problem for the last few years because of the substantial growth in the world population and their security requirements. There are numerous unsolved problems like crowd flow modelling and crowd behaviour detection, which are still open in this area, seeking great attention from the research community. Crowd flow modelling is one of such problems, and it is also an integral part of an intelligent surveillance system. Modelling of crowd flow has now become a vital concern in the development of intelligent surveillance systems. Real-time analysis of crowd behavior needs accurate models that represent crowded scenarios. An intelligent surveillance system supporting a good crowd flow model will help identify the risks in a wide range of emergencies and facilitate human safety. Mathematical models of crowd flow developed from real-time video sequences enable further analysis and decision making. A novel method identifying eight possible crowd flow behaviours commonly seen in the crowd video sequences is explained in this paper. The proposed method uses crowd flow localisation using the Gunnar-Farneback optical flow method. The Jacobian and Hessian matrix analysis along with corresponding eigenvalues helps to find stability points identifying the flow patterns. This work is carried out on 80 videos taken from UCF crowd and CUHK video datasets. Comparison with existing works from the literature proves our method yields better results.


2021 ◽  
pp. 388-397
Author(s):  
Jorge García-Gozález ◽  
Juan Miguel Ortiz-de-Lazcano-Lobato ◽  
Rafael Marcos Luque-Baena ◽  
Ezequiel López-Rubio

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5368
Author(s):  
Atul Sharma ◽  
Sushil Raut ◽  
Kohei Shimasaki ◽  
Taku Senoo ◽  
Idaku Ishii

This study develops a projector–camera-based visible light communication (VLC) system for real-time broadband video streaming, in which a high frame rate (HFR) projector can encode and project a color input video sequence into binary image patterns modulated at thousands of frames per second and an HFR vision system can capture and decode these binary patterns into the input color video sequence with real-time video processing. For maximum utilization of the high-throughput transmission ability of the HFR projector, we introduce a projector–camera VLC protocol, wherein a multi-level color video sequence is binary-modulated with a gray code for encoding and decoding instead of pure-code-based binary modulation. Gray code encoding is introduced to address the ambiguity with mismatched pixel alignments along the gradients between the projector and vision system. Our proposed VLC system consists of an HFR projector, which can project 590 × 1060 binary images at 1041 fps via HDMI streaming and a monochrome HFR camera system, which can capture and process 12-bit 512 × 512 images in real time at 3125 fps; it can simultaneously decode and reconstruct 24-bit RGB video sequences at 31 fps, including an error correction process. The effectiveness of the proposed VLC system was verified via several experiments by streaming offline and live video sequences.


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