Evidence filtering in a sequence of images for recognition

In recognizing a target object/entity with its attribute such as pose from images, the evidences extracted initially may be uncertain and/or ambiguous as they can only be defined probabilistically and/or do not satisfy the sufficient condition for recognition. These uncertainties and ambiguities associated with evidences are often due as much to the external, uncontrollable causes, such as the variation of illumination and texture distributions in the scene, as to the quality of the imaging tools used. This paper presents a method of filtering the uncertain and ambiguous evidences obtained from a sequence of images in such a way as to reach a reliable decision level for recognition. First, at each of the image sequence, a number of weak evidences are generated using 3D line, 3D shape descriptor and SIFT which may be ambiguous and/or uncertain to decide recognition quickly and reliably. To reach a faithful recognition, we need to enrich these evidences by Appearance vector and generate multiple interpretations of the target object with higher weights. We incorporate prior established Bayesian Evidence structure which embodied sufficient condition for recognition, to generate such interpretations. Furthermore, when robot moves, we do active recognition using particle filter framework in sequence of images to produce interpretation with highest weight and lowest error covariance. This paper provides readers with the details of the implementation and experimental results of Evidence Filtering in image sequences using Particle filter in HomeMate Robot application.