Open AccessIllumination invariant stationary object detection
Author(s) -
Hassan Waqas,
Birch Philip,
Mitra Bhargav,
Bangalore Nagachetan,
Young Rupert,
Chatwin Chris
Publication year - 2013
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2012.0054
Subject(s) - artificial intelligence , computer vision , computer science , segmentation , pixel , tracking (education) , object detection , invariant (physics) , image segmentation , video tracking , edge detection , pattern recognition (psychology) , object (grammar) , image (mathematics) , image processing , mathematics , mathematical physics , psychology , pedagogy
A real‐time system for the detection and tracking of moving objects that becomes stationary in a restricted zone. A new pixel classification method based on the segmentation history image is used to identify stationary objects in the scene. These objects are then tracked using a novel adaptive edge orientation‐based tracking method. Experimental results have shown that the tracking technique gives more than a 95% detection success rate, even if objects are partially occluded. The tracking results, together with the historic edge maps, are analysed to remove objects that are no longer stationary or are falsely identified as foreground regions because of sudden changes in the illumination conditions. The technique has been tested on over 7 h of video recorded at different locations and time of day, both outdoors and indoors. The results obtained are compared with other available state‐of‐the‐art methods.