Efficient Object Detection and Classification using Hybrid ELM with Analytic Hierarchy Process and Bayesian Network

Detecting object is one of the typical difficulties in computer technology which has its usage to surveillance, robotics, mul- timedia processing, and HCI. The multi-resolution framework is utilized by the proposed technique for object detection. In this efficient object detection, the lower resolution features are first used to discard the majority of negative windows at comparatively small cost, leaving a relatively small amount of windows to be processed in higher resolutions and this helps to attain better detection accuracy. Then the frameworks on Histograms of Oriented Gradient (HOG) features are used to detect the objects. For training and detection, the classifier used previously is Support Vector Machine (SVM) and Ex- treme Learning Machine (ELM). Hybrid ELM is used in the proposed technique to reduce the time for detection and im- prove the accuracy of classification. The input weights and hidden biases are created with the help of integrated Analytic Hierarchy Process (AHP) and Bayesian Network (BN) model. The experimental result shows that the proposed technique achieves better detection rate when compared to the existing techniques.