Elastic Grid-Based Multi-Fovea Algorithm for Real-Time Object-Motion Detection in Airborne Surveillance

2009 ◽  
pp. 181-213
Author(s):  
Balazs Gergely Soos ◽  
Vilmos Szabo ◽  
Csaba Rekeczky
Keyword(s):  
Real Time ◽  
Motion Detection ◽  
Object Motion ◽  
Elastic Grid ◽  
Grid Based

scholarly journals Real-time motion detection in H.264 compressed domain for surveillance application

2021 ◽  
Vol 1780 (1) ◽  
pp. 012032
Author(s):  
Young Keun Kim ◽  
Yeong Gyoo Jeon ◽  
Seung Ho Shin
Keyword(s):  
Real Time ◽  
Motion Detection ◽  
Compressed Domain ◽  
Time Motion ◽  
Surveillance Application

Detection of Human Body Motion Based on Electrostatic Measurements in the Indoor Environment

2011 ◽  
Vol 480-481 ◽  
pp. 1329-1334
Author(s):  
Wei Zheng ◽  
Zhan Zhong Cui
Keyword(s):  
Theoretical Analysis ◽  
Real Time ◽  
Human Body ◽  
Motion Detection ◽  
Average Velocity ◽  
Indoor Environment ◽  
Detection Method ◽  
Body Motion ◽  
The Real ◽  
Multiple Electrodes

An effective non-contact electrostatic detection method is used for human body motion detection. Theoretical analysis and pratical experiments are carried out to prove that this method is effective in the field of human body monitoring, in which a model for human body induced potential by stepping has been proposed. Furthermore, experiment results also prove that it’s feasible to measure the average velocity and route of human body motion by multiple electrodes array. What’s more the real-time velocity and direction of human body motion can be determined by orthogonal electrostatic detector array, and the real-time velocity and direction of human body motion can be obtained within the range of 2 meters.

MOVING CAMERA MOVING OBJECT SEGMENTATION IN COMPRESSED VIDEO SEQUENCES

2009 ◽  
Vol 09 (04) ◽  
pp. 609-627 ◽  
Author(s):  
J. WANG ◽  
N. V. PATEL ◽  
W. I. GROSKY ◽  
F. FOTOUHI
Keyword(s):  
Motion Detection ◽  
Object Segmentation ◽  
Object Motion ◽  
Moving Object ◽  
Transformation Model ◽  
Video Sequences ◽  
Compressed Domain ◽  
Camera Motion ◽  
Consistency Check ◽  
Moving Object Segmentation

In this paper, we address the problem of camera and object motion detection in the compressed domain. The estimation of camera motion and the moving object segmentation have been widely stated in a variety of context for video analysis, due to their capabilities of providing essential clues for interpreting the high-level semantics of video sequences. A novel compressed domain motion estimation and segmentation scheme is presented and applied in this paper. MPEG-2 compressed domain information, namely Motion Vectors (MV) and Discrete Cosine Transform (DCT) coefficients, is filtered and manipulated to obtain a dense and reliable Motion Vector Field (MVF) over consecutive frames. An iterative segmentation scheme based upon the generalized affine transformation model is exploited to effect the global camera motion detection. The foreground spatiotemporal objects are separated from the background using the temporal consistency check to the output of the iterative segmentation. This consistency check process can coalesce the resulting foreground blocks and weed out unqualified blocks. Illustrative examples are provided to demonstrate the efficacy of the proposed approach.

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