Content deleted Content added
remove predatory open access publisher |
Rescuing orphaned refs ("EHSV" from rev 808620881) |
||
Line 9:
People tracking from videos usually involves some form of [[background subtraction]] to segment foreground from background. Once foreground images are extracted, then desired algorithms (such as those for [[Video tracking|motion tracking]], [[Optical motion tracking|object tracking]], and [[facial recognition system|facial recognition]]) may be executed using these images.<ref name="TF" /><ref name="ITF">{{cite thesis |last=Abu|first=Patricia Angela|date=March 2015|title=Improving the Teknomo–Fernandez Background Image Modeling Algorithm for Foreground Segmentation|type=Ph.D|publisher=Ateneo de Manila University|url=https://www.researchgate.net/publication/273445070_Improving_the_Teknomo-Fernandez_Background_Image_Modeling_Algorithm_for_Foreground_Segmentation}}</ref>
However, [[background subtraction]] requires that the background image is already available<ref name="EHSV">{{cite journal | last1 = Abu | first1 = Patricia Angela | last2 = Fernandez | first2 = Proceso| title = Extending the of the Teknomo–Fernandez Background Image Generation Algorithm on the HSV Colour Space| url = http://www.wseas.org/multimedia/journals/information/2015/a465709-432.pdf }}</ref> and unfortunately, this is not always the case. Traditionally, the background image is searched for manually or automatically from the video images when there are no objects. More recently, automatic background generation through [[object detection]], [[medial filtering]], [[medoid filtering]], [[approximated median filtering]], [[linear predictive filter]], [[non-parametric model]], [[Kalman filter]], and [[adaptive smoothening]] have been suggested; however, most of these methods have high computational complexity and are resource-intensive.<ref name="TF" /><ref name="RTTF">{{cite conference |url=https://www.researchgate.net/publication/298791390_Modifying_the_Teknomo-Fernandez_Algorithm_for_Accurate_Real-Time_Background_Subtraction |title=Modifying the Teknomo–Fernandez Algorithm for Accurate Real-Time Background Subtraction | last1 = Abu | first1 = Patricia Angela | last2 = Fernandez | first2 = Proceso|date=March 2016| conference=Philippine Computing Science Congress}}</ref>
The Teknomo–Fernandez algorithm is also an automatic background generation algorithm. Its advantage, however, is its computational speed of only <math>O(R)</math>-time, depending on the resolution <math>R</math> of an image and its accuracy gained within a manageable number of frames. Only at least three frames from a video is needed to produce the background image assuming that for every pixel position, the background occurs in the majority of the videos. Furthermore, it can be performed for both grayscale and colored videos.<ref name="TF" />
| |||