Multiple object identification using grid voting of object center estimated from keypoint matches

This paper proposes a method to detect and identify multiple objects in an image using grid voting of object center positions estimated from local descriptor keypoint matches. For each keypoint match, the proposed method estimates the object center position using scale and orientation associated with the keypoints. Then, it casts a vote for an image grid where the estimated object center is located. For the grids with high number of votes, geometric verification of the keypoint matches is carried out to accurately localize multiple objects in the image. Since the computational complexity of the grid voting is O(n), where n is the number of estimated object centers, the proposed method runs faster than a conventional method using mean shift clustering with O(n²) complexity. Experimental results using images with 52 objects show that the proposed method reduces the computational time by approximately 60% compared to the conventional method, while identification accuracy is comparable. With the reduced computational complexity, industrial applications such as an efficient inventory management in retail using images are enabled in practical computational time.