Abstract In large crowd events, there is always a potential possibility that a stampede accident will occur. The accident may cause injuries or even death. Approaches for controlling crowd flows and predicting dangerous congestion spots would be a boon to on-site authorities to manage the crowd and to prevent fatal accidents. One of the most popular approaches is real-time crowd simulation based on position data from personal Global Positioning System (GPS) devices. However, the accuracy of spatial data varies for different GPS devices, and it is also affected by an environment in which an event takes place. In this paper, we would like to assess the effect of position errors on stampede prediction. We propose an Automatic Real-time dEtection of Stampedes (ARES) method to predict stampedes for large events. We implement three different stampede assessment methods in Menge framework and incorporate position errors. Our analysis suggests that the probability of simulated stampede changes significantly with the increase of the magnitude of position errors, which cannot be eliminated entirely with the help of classic techniques, such as the Kalman filter. Thus, it is our position that novel stampede assessment methods should be developed, focusing on the detection of position noise and the elimination of its effect.
Recent research has attempted to combine data from GPS and external sensors to improve the tracking accuracy. @cite_20 developed a positioning technique by integrating GPS and Wi-Fi data (to improve positioning indoors) and accelerometers (to distinguish pedestrian behavior). proposed an approach to utilize overhead Microsoft Kinect depth sensors placed in a walkway to provide reliable automatic pedestrian positioning for tracking @cite_16 . @cite_11 proposed to use infrared cameras to eliminate the multi-path errors which are caused by different positioning systems. Assisted-GPS (A-GPS) technique is also widely used to improve the performance of GPS-enabled smartphones @cite_5 . In general, combining GPS, WiFi, and sensors data improves tracking accuracy indoors (see @cite_22 for review). Besides employing external sensors, various algorithms were developed to mitigate GPS measurement noise, e.g., the Kalman filter @cite_43 @cite_54 and the autoregressive moving average filter @cite_36 .
@cite_22 @cite_36 @cite_54 @cite_43 @cite_5 @cite_16 @cite_20 @cite_11
2120824934 2100045669 2553223022 2506376746
Because of the high calculation cost and poor performance of a traditional planar map when dealing with complicated indoor geographic information, a WiFi fingerprint indoor positioning system cannot be widely employed on a smartphone platform. By making full use of the hardware sensors embedded in the smartphone, this study proposes an integrated approach to a three-dimensional (3D) indoor positioning system. First, an improved K-means clustering method is adopted to reduce the fingerprint database retrieval time and enhance positioning efficiency. Next, with the mobile phone’s acceleration sensor, a new step counting method based on auto-correlation analysis is proposed to achieve cell phone inertial navigation positioning. Furthermore, the integration of WiFi positioning with Pedestrian Dead Reckoning (PDR) obtains higher positional accuracy with the help of the Unscented Kalman Filter algorithm. Finally, a hybrid 3D positioning system based on Unity 3D, which can carry out real-time positioning for targets in 3D scenes, is designed for the fluent operation of mobile terminals. This paper addresses reliable and accurate indoor localization using inertial sensors commonly found on commodity smartphones. We believe indoor positioning is an important primitive that can enable many ubiquitous computing applications. To tackle the challenges of drifting in estimation, sensitivity to phone position, as well as variability in user walking profiles, we have developed algorithms for reliable detection of steps and heading directions, and accurate estimation and personalization of step length. We've built an end-to-end localization system integrating these modules and an indoor floor map, without the need for infrastructure assistance. We demonstrated for the first time a meter-level indoor positioning system that is infrastructure free, phone position independent, user adaptive, and easy to deploy. We have conducted extensive experiments on users with smartphone devices, with over 50 subjects walking over an aggregate distance of over 40 kilometers. Evaluation results showed our system can achieve a mean accuracy of 1.5m for the in-hand case and 2m for the in-pocket case in a 31m×15m testing area. As sensor-rich mobile devices became a commodity, more opportunities appeared for the creation of location-aware services. While GPS is a well established solution for outdoor localization, there is still no standard solution for localization indoors. This paper presents a novel accurate indoor positioning mechanism that is meant to run in common smartphones to be a readily and widely available solution. The system is based on multiple gait-model based filtering techniques for accurate movement quantification in combination with an advanced fused positioning mechanism that leverages sequences of opportunistic observations towards an accurate localization process. Magnetic field fluctuations, Wi-Fi readings and movement data are incrementally matched with a feature spot map containing multi-dimensional spatially-related features that characterize the building. A novel and convenient way of mapping the architectural and environmental properties of buildings is also introduced, which avoids the burden normally associated with the process. The system has been evaluated by multiple users in open and crowded spaces where overall median localization errors between 1.11 m and 1.68 m were obtained. While the reported errors are already satisfactory in the context of indoor localization, improvements may be readily achieved through the inclusion of additional reference features. High accuracy performance coupled with an opportunistic and infrastructure-free approach creates a very desirable solution for the indoor localization market doge. This paper presents an algorithm for navigating in challenging indoor environments that do not have WiFi. Dead-reckoning (DR) based on off-the-shelf smartphone sensors and magnetic matching (MM) based on indoor magnetic features are integrated. For DR, we utilize a two-filter algorithm structure and multi-level constraints to navigate under different human motion conditions. For MM, we use several approaches to enhance its performance. These approaches include multi-dimensional dynamic time warping, weighted k -nearest neighbor, and utilization of magnetic gradient fingerprints. Furthermore, realized that the key to enhance the DR MM performance is to mitigate the impact of MM mismatches, we introduced and evaluated two mismatch-detection approaches, including a threshold-based method that sets the measurement noises of MM positions based on their distances to the historical DR MM position solutions, and an adaptive Kalman filter-based method that introduces the estimation of the innovation sequence covariance into the calculation of the gain matrix instead of adjusting the measurement noises. The proposed mismatch-detection mechanism reduced the DR MM errors by 45.9 -67.9 in indoor tests with two smartphones, in two buildings, and under four motion conditions.