Cost-Effective Features for Reidentification in Camera Networks

Networks of smart cameras share large amounts of data to accomplish tasks such as reidentification. We propose a feature-selection method that minimizes the data needed to represent the appearance of objects by learning the most appropriate feature set for the task at hand (person reidentification). The computational cost for feature extraction and the cost for storing the feature descriptor are considered jointly with feature performance to select cost-effective good features. This selection allows us to improve intercamera reidentification while reducing the bandwidth that is necessary to share data across the camera network. We also rank the selected features in the order of effectiveness for the task to enable a further reduction of the feature set by dropping the least effective features when application constraints require this adaptation. We compare the proposed approach with state-of-the-art methods on the iLIDS and VIPeR datasets and show that the proposed approach considerably reduces network traffic due to intercamera feature sharing while keeping the reidentification performance at an equivalent or better level compared with the state of the art.