Detection of partially occluded objects – A comparative analysis based on Haar Classifier and K-Means Clustering

Abstract

This paper addresses the problem of detecting partially occluded objects from 2D images. The detection of partially occluded objects is performed and compared using feature-based training and color-based object segmentation. The occluded objects are very difficult to be detected based only on their features since, all the essential features may not be visible to the learned model due to occlusion. Haar cascade classifier has been utilised for feature-based training and the k-means clustering is utilized for color-based tracking. Various input images are provided Haar classifier as well as the K-means clustering to detect the objects in the 2D images and the subsequent results are compared and analysed. For segmenting the 2D objects using k-means clustering, the average recall and average precision varies from 0.70 to 0.98. The variation is based upon the veracity of the occluded objects. The average precision rate for detecting the occluded 2D objects through the developed method is between 0.24 and 0.60. And it is noted that the average recall for the respective detection lies between 0.25 and 0.70.