<p><strong>Abstract.</strong> Understanding the pattern of human activities benefits both the living service providing for the public and the policy-making for urban planners. The development of location-aware technology enables us to acquire large volume individual trajectories with different spatial and temporal resolution, such as GPS trajectories, mobile phone positioning data, social media check-in data, Wifi, and Bluetooth. However, the highest population penetrated mobile phone positioning trajectories are hard to infer human activity pattern directly, because of the sparsity in both space and time. This article presents a hierarchical clustering approach by using the move and stay sequences inferred from spare mobile phone trajectories to uncover the hidden human activity pattern. Personal stays at some places and following moves are first extracted from mobile phone trajectories, considering the spatial uncertainty of position. The similarity of trajectories is measured with a new indicator defined by the area of a spatial-temporal polygon bound with normalized trajectories. Finally, a hierarchical clustering method is developed to group trajectories with similar stay-move chains from the bottom to the top. The obtained clusters are analyzed to identify human activity patterns. An experiment with mobile phone users’ one-day trajectories in Shenzhen, China was conducted to test the performance of the proposed clustering approach. The results indicate all used trajectories are classified into 10 clusters representing typical daily activity patterns from the simple home-staying mode to complex home-working-social activity daily cycles. This study not only unravels the hidden activity patterns behind massive sparse trajectories but also deepens our understanding of the interaction of human activity and urban space.</p>

Abstract. Indoor positioning is of great importance to the era of mobile computing. Currently, much attention has been paid to RSS-based location for that it can provide position information without additional equipment. However, this method suffers from many challenges: (1) fingerprint ambiguity; (2) labor-intensive of fingerprint collection; (3) low efficiency of fingerprint matching. To get over these drawbacks, we provide a collaborative WiFi fingerprinting indoor positioning method using near relation. The base idea of this method is that interpolation method is used to enrich sparse Wi-Fi fingerprint. Near relation boundary is provided and Wi-Fi fingerprints is constrained to this region to reduce fingerprint ambiguity, which also can improve the efficiency of fingerprint matching. Extensive experiments show that a positioning accuracy of 3.8 m can be achieved with the near relation under 1 m interpolation density.

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Abstract. The demand for indoor localization has increased in fields such as indoor navigation, virtual reality and emergency response. Traditionally, hardware-based indoor positioning methods require a large number of devices to be deployed and require high maintenance costs. Vision-based localization methods offer a low-cost option for this purpose. Visual Localization has two typical pipeline: end-to-end study and traditional pose estimation based on PnP(Perspective-n-point). However, the quality of the retrieved images and 2D-3D correspondences is vital to the precision and recall of the traditional method. In this paper we try to partly overcome the mentioned drawback by eliminate the error retrieval images with multi-features, and we use several retrieved images to collect enough 2D-3D correspondences to improve the robustness against error input. We also filter the outliers during forming the 2D-3D correspondences with RANSAC and Lowe’s ratio test. As a supplement to the various indoor visual localization dataset production, we introduce a pipeline which can generate point clouds and mesh model via our integrated RGB-D cameras.