Person Re-ID is a challenging problem and is gaining more attention due to demands in security, intelligent system and other applications. Most person Re-ID works are vision-based, such as image, video, or broadly speaking, face recognition-based techniques. Recently, several multi-modal person Re-ID datasets were released, including RGB+IR, RGB+text, RGB+WiFi, which shows the potential of the multi-modal sensor-based person Re-ID approach. However, there are several common issues in public datasets, such as short time duration, lack of appearance change, and limited activities, resulting in un-robust models. For example, vision-based Re-ID models are sensitive to appearance change. In this work, a person Re-ID testbed with multi-modal sensors is created, allowing the collection of sensing modalities including RGB, IR, depth, WiFi, radar, and audio. This novel dataset will cover normal daily office activities with large time span over multi-seasons. Initial analytic results are obtained for evaluating different person Re-ID models, based on small datasets collected in this testbed.

Recently, large-scale spectrum measurements show that geo-location spectrum databases, as recommended by regulators (e.g., FCC, Ofcom, ECC) for TV white space (TVWS) discovery are notoriously inaccurate in Metropolitan areas because of inaccurate TV channel propagation models they adopted. To counter this challenge, we propose a cloud-based crowd-augmented spectrum mapping scheme. Our scheme aims to build accurate geo-location database in Metropolitan areas with high spatial resolution under minimum cost by jointly utilizing superior computing capacity of cloud servers, abundant spectrum sensing data from crowd of mobile white-spaces device (WSD) users and the well-established geo-statistical techniques. More specifically, our spectrum mapping scheme consists of three interdependent components (1) opportunistic mobile spectrum sensing, which exploits the high spatial diversity of mobile users along with low-cost embedded spectrum measuring solution to retrieve power spectrum density (PSD) information of the TV channels in a large Metropolitan region; (2) cloud-based geo-statistical spectrum mapping, which estimates PSD at unknown geographic locations by utilizing the abundant PSD data aggregated at the cloud server consisting of geo-statistical analysis and interpolation tools; (3) optimal spatial sampling, which further augments the accuracy of the spectrum map by selecting the optimal locations, at which additional spectrum sensing measurements are obtained to minimize the spectrum mapping error. To verify the performance of the proposed scheme, an experiment is conducted in our university campus. The experiment result shows that our proposed scheme can discover more spectrum opportunities than the information reported by commercially available geo-location spectrum databases.