Unsupervised approach for object matching using Speeded Up Robust Features

Autonomous object counting system is of great use in retail stores, industries and also in research processes. In this paper, a Speeded Up Robust Feature (SURF) based robust algorithm for identifying, counting and locating all instances of a defined object in any image, has been proposed. The defined object is referred to as prototype and the image in which one wishes to count the prototype is referred to as scene image. The algorithm starts by detecting the interest points for SURF in both, prototype and scene images. The SURF points on prototype are first clustered using density based clustering; then SURF points in each cluster are matched with those in scene image. The SURF points in scene image that have been matched w.r.t. a single cluster, are clustered using the same clustering algorithm. Each cluster formed in scene image represents an instance of prototype object in the image. Homography transforms are further used to give exact location and span of each prototype object in the scene image. Once the span of each prototype is defined, SURF points within this span are matched with the prototype image and then Homography transform is once again applied while considering the newly matched SURF points; thus eliminating noisy detection/s of prototype. While the same process is repeated with each cluster, a novel centroid based algorithm for merging repeated detections of same prototype instance is used. Carrying the benefits of SURF and Homography transforms, the algorithm is capable of detecting all prototype instances present in scene image, irrespective of their scale and orientation. The complete algorithm has also been integrated into a desktop application, which uses camera feed to report the real time count of the prototype in the scene image.