scholarly journals Pixel-wise motion detection in persistent aerial video surveillance

2012 ◽  
Author(s):  
Grace Vesom
Keyword(s):  
Video Surveillance ◽  
Motion Detection ◽  
Aerial Video

scholarly journals Accelerated RANSAC for Accurate Image Registration in Aerial Video Surveillance

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 36775-36790
Author(s):  
Jin Zheng ◽  
Wei Peng ◽  
Yue Wang ◽  
Bo Zhai
Keyword(s):  
Image Registration ◽  
Video Surveillance ◽  
Aerial Video

Aerial video surveillance and exploitation

2001 ◽  
Vol 89 (10) ◽  
pp. 1518-1539 ◽  
Author(s):  
R. Kumar ◽  
H. Sawhney ◽  
S. Samarasekera ◽  
S. Hsu ◽  
Hai Tao ◽  
...  
Keyword(s):  
Video Surveillance ◽  
Aerial Video

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.

Context aided tracking in aerial video surveillance

2008 ◽  
Author(s):  
Scott J. Pierce ◽  
Juan R. Vasquez
Keyword(s):  
Video Surveillance ◽  
Aerial Video
Sign in / Sign up

Export Citation Format

Share Document