20,290
edits
GStreamer DispTEC Motion Detection Plugin: Difference between revisions
GStreamer DispTEC Motion Detection Plugin (view source)
Revision as of 07:16, 9 February 2020
, 9 February 2020no edit summary
No edit summary |
mNo edit summary |
||
| Line 1: | Line 1: | ||
<seo title="GStreamer Motion Detection | GStreamer DispTEC | RidgeRun" titlemode="replace" keywords="Motion Detection,DispTEC,GStreamer DispTEC,GStreamer Motion Detection, description="Learn about GstDispTEC Motion Detection from the RidgeRun Developer Wiki."></seo> | |||
<table cellspacing="0"> | <table cellspacing="0"> | ||
<tr> | <tr> | ||
| Line 180: | Line 182: | ||
</td></tr></table> | </td></tr></table> | ||
= Overview = | = GStreamer DispTEC Motion Detection Overview = | ||
The GstDispTEC motion detection is a GStreamer element which integrates DispTEC <ref>http://palvarado.ietec.org/pmwiki/index.php/Proyectos/DispTEC?userlang=en</ref> library's algorithms for motion detection, making it possible to incorporate their functionality into GStreamer pipelines, such as motion detection in a video sequence, even with non-stationary cameras! This allows the user to strengthen image analysis, by connecting its own applications to the pipeline for receiving the analysis data and much more amazing things, as shown in the following sample video. Also, the algorithms can be run either on CPU or GPU, making the most of the computational resources and reducing the processing time. | The GstDispTEC motion detection is a GStreamer element which integrates DispTEC <ref>http://palvarado.ietec.org/pmwiki/index.php/Proyectos/DispTEC?userlang=en</ref> library's algorithms for motion detection, making it possible to incorporate their functionality into GStreamer pipelines, such as motion detection in a video sequence, even with non-stationary cameras! This allows the user to strengthen image analysis, by connecting its own applications to the pipeline for receiving the analysis data and much more amazing things, as shown in the following sample video. Also, the algorithms can be run either on CPU or GPU, making the most of the computational resources and reducing the processing time. | ||
| Line 188: | Line 190: | ||
</center> | </center> | ||
= Description = | = Motion Detection Description = | ||
The motion detection element is based on DispTEC's MCD and SCBU algorithms, designed to run on both CPU and GPU. Its primary feature is the motion detection inside a non-stationary capture, although it can also be used on stationary captures. When deciding which of the algorithms to use, consider the following guidelines <ref>https://www.cv-foundation.org/openaccess/content_cvpr_workshops_2013/W03/papers/Yi_Detection_of_Moving_2013_CVPR_paper.pdf</ref>: | The motion detection element is based on DispTEC's MCD and SCBU algorithms, designed to run on both CPU and GPU. Its primary feature is the motion detection inside a non-stationary capture, although it can also be used on stationary captures. When deciding which of the algorithms to use, consider the following guidelines <ref>https://www.cv-foundation.org/openaccess/content_cvpr_workshops_2013/W03/papers/Yi_Detection_of_Moving_2013_CVPR_paper.pdf</ref>: | ||
| Line 214: | Line 216: | ||
The varying learning rate property is a key feature if trying to detect motion inside of non-static captures, given that it allows adaptation to object movement vs background movement. Increasing the learning rate decreases the sensibility to object motion detection, but improves the rate at which it adapts to background changes. A low maximum value helps in noisy captures due to the fact that the detected moving objects won't be absorbed as background. | The varying learning rate property is a key feature if trying to detect motion inside of non-static captures, given that it allows adaptation to object movement vs background movement. Increasing the learning rate decreases the sensibility to object motion detection, but improves the rate at which it adapts to background changes. A low maximum value helps in noisy captures due to the fact that the detected moving objects won't be absorbed as background. | ||
|} | |} | ||
= Architecture = | = Motion Detection Architecture = | ||
GstDispTEC motion detection has been created based on the DispTEC library. However, it is independent of the DispTEC's dependencies and it can be easily integrated whichever library that DispTEC is based on (OpenCV, LTI-lib, etc). This is achieved by an ''Abstraction layer'' just on the base of the plug-in, which can adapt it to several DispTEC variations. | GstDispTEC motion detection has been created based on the DispTEC library. However, it is independent of the DispTEC's dependencies and it can be easily integrated whichever library that DispTEC is based on (OpenCV, LTI-lib, etc). This is achieved by an ''Abstraction layer'' just on the base of the plug-in, which can adapt it to several DispTEC variations. | ||