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Content Provider | IET Digital Library |
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Author | Hassan, Waqas Birch, Philip Mitra, Bhargav Bangalore, Nagachetan Young, Rupert Chatwin, Chris |
Abstract | 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. |
Starting Page | 1 |
Ending Page | 8 |
Page Count | 8 |
ISSN | 17519632 |
Volume Number | 7 |
e-ISSN | 17519640 |
Issue Number | Issue 1, Feb (2013) |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/iet-cvi/7/1 |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2012.0054 |
Journal | IET Computer Vision |
Access Restriction | Open |
Rights Holder | © The Institution of Engineering and Technology |
Subject Keyword | Adaptive Edge Orientation-based Tracking Method Computer Vision And Image Processing Technique Foreground Region Historic Edge Maps Illumination Condition Illumination Invariant Stationary Object Detection Image Classification Image Segmentation Image Segmentation History Moving Object Detection Moving Object Tracking Object Detection Object Tracking Optical, Image And Video Signal Processing Pixel Classification Method Real-time System Video Recording Video Signal Processing |
Content Type | Text |
Resource Type | Article |
Subject | Computer Vision and Pattern Recognition Software |
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