Object Detection In Cluttered Background using Color Clusters
Chetan S. Gode1, Atish S. Khobragade2

1Chetan S. Gode*, Department of Electronics and Telecommunication Engineering, Yeshwantrao Chavan College of Engineering, Nagpur, India.
1Atish S. Khobragade, Department of Electronics Engineering, Yeshwantrao Chavan College of Engineering, Nagpur, India.
Manuscript received on September 20, 2019. | Revised Manuscript received on October 15, 2019. | Manuscript published on October 30, 2019. | PP: 1241-1247 | Volume-9 Issue-1, October 2019 | Retrieval Number: A9620109119/2019©BEIESP | DOI: 10.35940/ijeat.A9620.109119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Object detection in presence of complex background and illumination variation is important image analysis problem with many applications. Most of the object detection algorithms use local image descriptors which are computed from interest points based on luminance information and neglect precious color information of an object. If appearances of the object to be detected contain multiple colors in non-homogeneous distributions then it makes it difficult to detect these objects using shape features. In this context, we propose a robust algorithm designed to detect a class of objects using a descriptor which is computed from color information of an object. Clusters are formed in Hue and Saturation (HS) color space of an object using k-means clustering and cluster analysis based on number of pixels belong to each cluster, object detection is performed. Use of clustering algorithm in color space of an object to form descriptor reduces the large dimensionality of the histogram bins in the computation. The performance of the algorithm is demonstrated by experimentation carried out on standard dataset GroZi-120. Experimental results shows that the proposed algorithm is insensitive to scaling, object rotation, illumination variations and capable of handling cluttered background effectively. Finally results shows that proposed algorithm outperforms closely related algorithm by a decisive margin.
Keywords: Object detection, k-mean clustering, Target image, Image descriptor, and background subtraction.