This poster introduces an Augmented Reality (AR)-assisted framework to help exclude Non-Line-of-Sight (NLOS) signals from the Global Navigation Satellite Systems (GNSS). We developed an AR mobile app named GnssQuest, augmenting the user's real-time camera view with a visualization of GNSS satellites. Our real-world experiment demonstrates that GnssQuest helps users to exclude NLOS satellites blocked by surrounding buildings, leading to significant improvements in GNSS positioning performance.

In the realm of video analytics, the significance of video anomaly detection cannot be ignored. Many video anomaly detection methods have been introduced in traffic scenarios, and they have achieved some promising results. However, there still exists a large room for improving the traffic anomaly detection performance. In this paper, we present an end-to-end approach, the Dual-Stream Anomaly Detection Network (DS-ADN), specialized for traffic anomaly detection. DS-ADN introduces optical flow data and utilizes inter-flow information interaction coupled with the Multi-Scale Attention Fusion Module (MSA-FM) to comprehensively merge RGB and optical flow data. It features an innovative depth-based prediction loss function that adapts to the vehicle’s regular shape and events at different distances under the surveillance camera. This adaptation occurs through block-wise average pooling and weighted allocation across varying depths. Considering the prevalent issues of datasets dedicated to traffic anomaly detection generally containing a single type of anomaly, this paper introduces the Traffic Surveillance Dataset (TSD) containing multiple anomalies for model validation. Extensive experiments on the TSD dataset highlight the better performance of our DS-ADN, manifesting an AUC gain of over 8.8% compared to other state-of-the-art models. Moreover, DS-ADN achieves competitive performance across three widely-used anomaly detection datasets that contain multiple anomalies, including UCSD Ped2, Avenue, and ShanghaiTech, demonstrating its generalization ability.

OntoWebber is a system for managing data on the Web with formally encoded semantics. It aims at solving the problems current technologies are confronted with, namely, the reusability of software components, flexibility in personalization, and ease of maintenance for data intensive Web sites. Based on a domain ontology and a site modeling ontology, site views on the underlying data can be constructed as site models. Instantiation of these models will create a browsable Web site, and manipulation of the site models helps to reduce the high effort of personalizing and maintaining the Web site. In this paper we present the architecture and demonstrate the major components of the system.