1. WarpMap: Accurate and Efficient Indoor Locating by Dynamic Warping in Sequence-type Radio-map
Xuehan Ye，Yongcai Wang，Wei Hu，Lei Song，Zhaoquan Gu，Deying Li
2. IntenCT: Efficient Multi-Target Counting and Tracking By Binary Proximity Sensors
Yongcai Wang，Lei Song，Zhaoquan Gu，Deying Li
Abstract: Radio-map based method has been widely used for indoor location and navigation, but remained key challenges are: 1) laborious efforts to calibrate a fine-grained radio-map, and 2) the locating result inaccuracy and not robust problems due to random RSS noises. An efficient way to overcome these problems is to collect RSS signatures along indoor paths and utilize sequence matching to enhance the location robustness. But, due to problems of indoor path combinatorial explosion, random RSS loss during movement, and moving speed disparity during online and offline phases, how to exploit sequence matching in radio-map remains difficult. This paper proposes WarpMap, an efficient sequence-type radio-map model and an accurate indoor location method by dynamic warping in sequence-type radio-map. Its distinct features include 1) an undirected graph model (Trace-graph) for efficiently calibrating and storing sequence-type radio-map. It overcomes the path combinatorial explosion, and RSS miss-of-detection problems; and 2) a sub-sequence dynamic time warping (SDTW) algorithm for accurate and efficient on-line locating. We show SDTW is efficient and can tolerate random RSS disparities at discrete points and can handle the moving speed differences in on-line and off-line phases. The impacts of different warping distance functions, RSS preprocessing techniques were also investigated. Extensive experiments in office environments verified the efficiency and accuracy of WarpMap, which can calibrated within several minutes for 1000m 2 area and provides overall nearly 20% accuracy improvements than the state-of-the-art of radio-map method.
Abstract: Binary proximity sensors (BPS) is a generic model for many non-collaborative, presence detecting sensor. It outputs “1” when one or more targets are presenting in its sensing range and “0″ otherwise. It cannot tell the number of targets nor the targets’ identities in its sensing range. But for its privacy protection and device-free properties, BPS-based tracking has attracted great attentions. However, multiple target counting and tracking (MTCT) by BPS network remains very challenging. Existing approaches generally rely on trajectory decomposition, which suffer association complexity issue and can hardly provide accurate results. To address these challenges, this paper presents an novel intensity-based counting and tracking approach, called IntenCT, which tracks the evolvement of the multi-targets’ probabilistic density distribution overtime, without the complexity of enumerating the multiple targets’ trajectories. Then clustering algorithms on the density distribution are proposed to find the target groups, and count the targets in each group by calculating the integral of the density distribution in the group region. At last, the trajectories of the separable targets in each group are estimated using $K$-means and a motion consistency model. Extensive analysis and simulations show that IntenCT has quadratic complexity which is very efficient; provides the current best known multi-target counting lower bound; and tracks the multi-targets more accurately than the existing approaches.