scholarly journals Fast Compressed Domain Motion Detection in H.264 Video Streams for Video Surveillance Applications

2009 ◽  
Author(s):  
Krzysztof Szczerba ◽  
Søren Forchhammer ◽  
Jesper Stottrup-Andersen ◽  
Peder Tanderup Eybye
Keyword(s):  
Video Surveillance ◽  
Motion Detection ◽  
Compressed Domain ◽  
Video Streams ◽  
Domain Motion ◽  
Surveillance Applications

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.

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

Compressed-domain motion detection for efficient and error-resilient MPEG-2 to H.264 transcoding

2007 ◽  
Author(s):  
Jan Lievens ◽  
Peter Lambert ◽  
Dieter Van de Walle ◽  
Freya Dawoud ◽  
Joeri Barbarien ◽  
...  
Keyword(s):  
Motion Detection ◽  
Compressed Domain ◽  
Error Resilient ◽  
Domain Motion

scholarly journals Foreground Segmentation in Depth Imagery Using Depth and Spatial Dynamic Models for Video Surveillance Applications

Sensors ◽  
2014 ◽  
Vol 14 (2) ◽  
pp. 1961-1987 ◽  
Author(s):  
Carlos del-Blanco ◽  
Tomás Mantecón ◽  
Massimo Camplani ◽  
Fernando Jaureguizar ◽  
Luis Salgado ◽  
...  
Keyword(s):  
Video Surveillance ◽  
Dynamic Models ◽  
Foreground Segmentation ◽  
Spatial Dynamic ◽  
Surveillance Applications

Compressed domain human action recognition in H.264/AVC video streams

2014 ◽  
Vol 74 (21) ◽  
pp. 9323-9338 ◽  
Author(s):  
Manu Tom ◽  
R. Venkatesh Babu ◽  
R Gnana Praveen
Keyword(s):  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Compressed Domain ◽  
Video Streams

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
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