Fast motion detection in compressed domain for video surveillance

2002 ◽  
Vol 38 (9) ◽  
pp. 409 ◽  
Author(s):  
J. Pons ◽  
J. Prades-Nebot ◽  
A. Albiol ◽  
J. Molina
Keyword(s):  
Video Surveillance ◽  
Motion Detection ◽  
Compressed Domain ◽  
Fast Motion

Fast motion detection in coded video streams for a large-scale remote video sensor system

2014 ◽  
Author(s):  
Yong-Sung Kim ◽  
Gyu-Hee Park ◽  
Seung-Hwan Kim ◽  
Hyung-Joon Cho
Keyword(s):  
Motion Detection ◽  
Large Scale ◽  
Sensor System ◽  
Video Streams ◽  
Fast Motion ◽  
Video Sensor

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.

Abnormal Motion Detection for Intelligent Video Surveillance

2011 ◽  
Vol 58-60 ◽  
pp. 2290-2295 ◽  
Author(s):  
Ruo Hong Huan ◽  
Xiao Mei Tang ◽  
Zhe Hu Wang ◽  
Qing Zhang Chen
Keyword(s):  
Kalman Filter ◽  
Object Tracking ◽  
Video Surveillance ◽  
Motion Detection ◽  
Background Subtraction ◽  
Moving Objects ◽  
Video Streams ◽  
Intelligent Video Surveillance ◽  
Tracking Process ◽  
Object Parameters

A method of abnormal motion detection for intelligent video surveillance is presented, which includes object intrusion detection, object overlong stay detection and object overpopulation detection. Background subtraction algorithm is used to detect moving objects in video streams. Kalman filter is applied for object tracking. By the construction of relation matrix, the tracking process is divided into five statuses for prediction and estimation, which are object disappearing, object separating, new object appearing, object sheltering and object matching. The object parameters and predictive information in the next frame which is used to track moving objects is established by Kalman filter. Then, three types of abnormal motion detection are implemented. The relative position of alarm area or guard line with the rectangle boxes of the moving objects is used to detect whether the object is invading. The existing time of the moving objects in monitor area is counted to detect whether the object is staying too long. Moving objects in the monitor area are classified and counted to detect whether the objects are too much. Alarm will be triggered when abnormal motion detection as defined is detected in the monitor area.

Design on Intelligent Video Surveillance System Based on Target Identification Algorithm

2015 ◽  
Vol 713-715 ◽  
pp. 460-465 ◽  
Author(s):  
Zong Jie Meng ◽  
Cai Jie
Keyword(s):  
Video Surveillance ◽  
Motion Detection ◽  
Target Identification ◽  
Identification Algorithm ◽  
Accuracy Rate ◽  
Intelligent Video Surveillance ◽  
Frame Difference ◽  
Adjacent Frame ◽  
Intelligent Video Surveillance System ◽  
Hardware Realization

This paper makes study on the adjacent frame difference and algorithm realization of SOM i8dentification after improvement, of which it includes motion detection, target identification; the realized video surveillance module makes up the intelligent video surveillance that can reconstruct platform. Motion detection module adopts algorithm of adjacent frame difference after improvement, which can correctly mark the motion object. Target identification module adopts self-mapping nerve net after improvement, it is easier for hardware realization, and meanwhile the accuracy rate of identification is equal to classical algorithm.

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