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

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.