An Innovative Tree Gradient Boosting Method Based Image Object detection from a Uniform Background Scene

Abstract

Object detection is a wide area problem domain in the field of computer and machine vision. Complex background adds challenge and error margin as well to the problem significantly lot algorithms for object detection are hard to comply with occlusion and pixel bending moment affect. In this paper a highly robust algorithm ORBTRIAN for a less resolution image has been proposed and implemented using ORB detection with gradient boosting machine learning algorithm. The work has been compared with Adaboost and Surf based technology. The analysis shows 3.8% increase in performance of earlier model. The feature points extracted from ORB method are further processed to reduce the processing further. Only those points are selected which are triangularly farthest from centroid of it and only 1 point of feature selected. Thus the result is around 28%, much faster than earlier computation. The tree based GB has been implemented in this algorithm. With more number of feature points more classes need to be recognized and hence the computations performed is required an unreasonable amount of effort and time. So some nearby classes are assigned at same level using our algorithm to reduce the number of tree nodes. Overall performance of the proposed algorithm shows a significant increase in efficiency in computation time.