PEDESTRIAN DETECTION IN INFRARED IMAGES: INTERVAL VALUED REPRESENTATION BASED APPROACH

Abstract

The evaluation in the cost of infrared cameras opens a new platform for attacking many untouched vision
problems. This article proposes a novel method of representing infrared images by the use of edgelet features
for object recognition application. The proposed technique makes use of interval valued representation for
edgelet features of the infrared images. A scheme of identification of the objects based on the proposed features
extraction and representation model is also designed. Hence the features are transformed in interval valued
representation, the proposed model drastically reduces the dimension of the feature space which in turn reduces
the computational time for object recognition in the infrared images. Further an extensive experimentation is
conducted on publically available datasets. The result of the experiments tells us that the proposed algorithm
outperforms for the state of the art technique. However, the main advantage of the proposed technique is that it
takes relatively a less time for identification as it depends on a simple matching strategy.