Comparative analysis of PRID algorithms based on results ambiguity evaluation

Abstract

The re-identification of a subject among different cameras (namely Person Re-Identification or PRID) is a task that implicitly defines ambiguities. Two individuals dressed in a similar manner or with a comparable body shape are likely to be misclassified by a computer vision system, especially when only poor quality images are available (i.e. the case of many surveillance systems). For this reason we introduce a method to find, exploit and classify ambiguities among the results of PRID algorithms. This approach is useful to analyze the results of a classical PRID pipeline on a specific dataset evaluating its effectiveness in re-identification terms with respect to the ambiguity rate (AR) value. Cumulative Matching Characteristic curves (CMC) can be consequently split according to the AR, using the proposed method to evaluate the performance of an algorithm in low, medium or high ambiguity cases. Experiments on state-of-art algorithms demonstrate that ambiguity-wise separation of results is an helpful tool in order to better understand the effective behaviour of a PRID approach.