In this paper we study techniques for accurate detection, localization, and tracking of multiple people in an indoor scene covered by multiple top-view fisheye cameras. This is a rarely studied setting within the topic of multi-camera object tracking. The experimental results on test videos exhibit good performance for practical use. We also propose methods to account for occlusion by scene objects at different stages of the algorithm that lead to improved results.

Camera-enabled mobile devices are commonly used as interaction platforms for linking the user's virtual and physical worlds in numerous research and commercial applications, such as serving an augmented reality interface for mobile information retrieval. The various application scenarios give rise to a key technique of daily life visual object recognition. On-premise signs (OPSs), a popular form of commercial advertising, are widely used in our living life. The OPSs often exhibit great visual diversity (e.g., appearing in arbitrary size), accompanied with complex environmental conditions (e.g., foreground and background clutter). Observing that such real-world characteristics are lacking in most of the existing image data sets, in this paper, we first proposed an OPS data set, namely OPS-62, in which totally 4649 OPS images of 62 different businesses are collected from Google's Street View. Further, for addressing the problem of real-world OPS learning and recognition, we developed a probabilistic framework based on the distributional clustering, in which we proposed to exploit the distributional information of each visual feature (the distribution of its associated OPS labels) as a reliable selection criterion for building discriminative OPS models. Experiments on the OPS-62 data set demonstrated the outperformance of our approach over the state-of-the-art probabilistic latent semantic analysis models for more accurate recognitions and less false alarms, with a significant 151.28% relative improvement in the average recognition rate. Meanwhile, our approach is simple, linear, and can be executed in a parallel fashion, making it practical and scalable for large-scale multimedia applications.

We propose MobileQueue, a mobile queue-card management system that offers more freedom to customers by enabling image-based queue-card retrieving and service-information querying actions using mobile phones. MobileQueue interacts with cloud services allowing customers to query summary description and availability (e.g., available seats) of services provided by stores. MobileQueue also offers suggestions to waiting customers such as potentially interesting substitute activities and stores.

Wi-Fi ingerprint-based localization is currently the most promising approach to building metropolitan-scale localization systems for both indoor and outdoor environments. A Wi-Fi fingerprint localization system makes use of a radio map that contains Wi-Fi signals received at various locations, and uses the map for location estimation. Thus, the accuracy of a Wi-Fi fingerprint localization system greatly depends on the quality of its radio map. This paper explores various properties of a metropolitan radio map that may affect the accuracy of Wi-Fi fingerprint localization systems. In this study, metropolitan-scale radio maps are obtained using war walking and war driving. These maps contain hundreds of thousands of access points and signal samples. A detailed comparison analysis of selected radio map properties reveals how different map properties affect the difference between the positional accuracies of the driving and walking radio maps in Wi-Fi fingerprint-based localization.

Wi-Fi based indoor location systems have been shown to be both cost-effective and accurate, since they can attain meter-level positioning accuracy by using existing Wi-Fi infrastructure in the environment. However, two major technical challenges persist for current Wi-Fi based location systems, instability in positioning accuracy due to changing environmental dynamics, and the need for manual offline calibration during site survey. To address these two challenges, three environmental factors (people, doors, and humidity) that can interfere with radio signals and cause positioning inaccuracy are identified. Then, we have proposed a sensor-assisted adaptation method that employs RFID sensors and environment sensors to adapt the location systems automatically to the changing environmental dynamics. The proposed adaptation method performs online calibration to build multiple context-aware radio maps under various environmental conditions. Experiments were performed on the sensor-assisted adaptation method. The experimental results show that the proposed adaptive method can avoid adverse reduction in positioning accuracy under changing environmental dynamics.