CMV100: A dataset for people tracking and re-identification in sparse camera networks

摘要

This paper introduces CMV100, a new research dataset for people tracking and re-identification in sparse camera networks. Baseline methods for reidentification performance analysis are also proposed. The dataset consist of over 400 indoor video sequences in total. The number of visually distinctive human objects is 100, and each person appears in three different views on average and in five at maximum. The dataset evaluation is performed using sequence matching methods based on adaptive boosting and CART decision trees. The preliminary experiments show the challenging characteristics of the new dataset and serve as a practical starting point for future improvements.