Posterior probability measure for image matching

摘要

Template matching is one of the principle techniques in visual tracking. Various similarity measures have been developed to find the target in an acquired image by matching with a template. However, mismatching or misidentification may sporadically occur due to the influence of the background pixels included in the designated target model. Taking into account the statistical features of a search region, a novel similarity measure is proposed, which can decrease the interference of the background pixels enclosed in the model. It highlights the significant target features and at the same time reduces the influence of the features shared by both the target and the background. It exhibits an excellent monotonic property and a distinct peak-like distribution. This new measure is also demonstrated to have a direct interpretation of posterior probability and is named as posterior probability measure (PPM). The proposed PPM can be obtained through a pixel-wise computation and exhibits suitability for image matching. The pixel-wise computation also enables a fast measure update after a target region has changed, which results in a new adaptive scaling method for tracking a target with a varying size. Experiments show that it provides a higher precision in the localization and a discriminatory power superior to the existing similarity measures, such as Bhattacharyya coefficient, Kullback–Leibler divergence, and normalized cross correlation. The effectiveness of the adaptive scaling method is demonstrated in experiments.