WHoG: a weighted HOG-based scheme for the detection of birds and identification of their poses in natural environments

Karmaker, Debajyoti, Schiffner, Ingo, Strydom, Reuben and Srinivasan, Mandyam V (2017). WHoG: a weighted HOG-based scheme for the detection of birds and identification of their poses in natural environments. In: 2016 14th International Conference on Control, Automation, Robotics and Vision, ICARCV 2016. 14th International Conference on Control, Automation, Robotics and Vision, ICARCV 2016, Phuket, Thailand, (). 13 - 15 November 2016. doi:10.1109/ICARCV.2016.7838650


Author Karmaker, Debajyoti
Schiffner, Ingo
Strydom, Reuben
Srinivasan, Mandyam V
Title of paper WHoG: a weighted HOG-based scheme for the detection of birds and identification of their poses in natural environments
Conference name 14th International Conference on Control, Automation, Robotics and Vision, ICARCV 2016
Conference location Phuket, Thailand
Conference dates 13 - 15 November 2016
Convener IEEE
Proceedings title 2016 14th International Conference on Control, Automation, Robotics and Vision, ICARCV 2016
Journal name 2016 14th International Conference on Control, Automation, Robotics and Vision, ICARCV 2016
Place of Publication Piscataway, NJ, United States
Publisher Institute of Electrical and Electronics Engineers
Publication Year 2017
Sub-type Fully published paper
DOI 10.1109/ICARCV.2016.7838650
Open Access Status Not yet assessed
ISBN 9781509035496
9781509047574
9781509035502
Total pages 7
Collection year 2018
Language eng
Abstract/Summary We describe a technique for object detection that uses a combination of global shape descriptors and local point descriptors. Our system is able to represent pose using a global shape descriptor, rather than the commonly used part based representation. This approach considerably reduces computational complexity and achieves a significant performance improvement on an extensive dataset: CUB-200-2011 [31]. Our methodology is valuable for the detection of textured objects that are viewed against background clutter and possess a high degree of articulation and variation of pose, as for example in birds. We demonstrate how high and low frequency gradients can be separated to better deal with the presence of interfering textures or stripes within the body, which is a major problem in the detection of bird-like objects. Furthermore, detection accuracy is improved by integrating appropriately designed scale invariant color features into the algorithm.
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

 
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