An agent-based WCET analysis for top-view person re-identification

Person re-identification is a challenging task for improving and personalising the shopping experience in an intelligent retail environment. A new Top View Person Re-Identification (TVPR) dataset of 100 persons has been collected and described in a previous work. This work estimates the Worst Case Execution Time (WCET) for the features extraction and classification steps. Such tasks should not exceed the WCET, in order to ensure the effectiveness of the proposed application. In fact, after the features extraction, the classification process is performed by selecting the first passage under the camera for training and using the others as the testing set. Furthermore, a gender classification is exploited for improving retail applications. We tested all feature sets using k-Nearest Neighbors, Support Vector Machine, Decision Tree and Random Forest classifiers. Experimental results prove the effectiveness of the proposed approach, achieving good performance in terms of Precision, Recall and F1-score.