Map-matching is the process of aligning a sequence of observed user positions with the road network on a digital map. It is a fundamental pre-processing step for many applications, such as moving object management, traffic flow analysis, and driving directions. In practice there exists huge amount of low-sampling-rate (e.g., one point every 2--5 minutes) GPS trajectories. Unfortunately, most current map-matching approaches only deal with high-sampling-rate (typically one point every 10--30s) GPS data, and become less effective for low-sampling-rate points as the uncertainty in data increases. In this paper, we propose a novel global map-matching algorithm called ST-Matching for low-sampling-rate GPS trajectories. ST-Matching considers (1) the spatial geometric and topological structures of the road network and (2) the temporal/speed constraints of the trajectories. Based on spatio-temporal analysis, a candidate graph is constructed from which the best matching path sequence is identified. We compare ST-Matching with the incremental algorithm and Average-Frechet-Distance (AFD) based global map-matching algorithm. The experiments are performed both on synthetic and real dataset. The results show that our ST-matching algorithm significantly outperform incremental algorithm in terms of matching accuracy for low-sampling trajectories. Meanwhile, when compared with AFD-based global algorithm, ST-Matching also improves accuracy as well as running time.

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Map-matching for low-sampling-rate GPS trajectories

A method for tagging digital media is described. The method includes selecting a digital media and selecting region within the digital media. The method may further include associating a person or entity with the selected region and sending a notification of the association the person or entity or a different person or entity. The method may further include sending advertising with the notification.

Tagging Digital Media

An embodiment provides for enabling retrieval of a collection of captured images that form at least a portion of a library of images. For each image in the collection, a captured image may be analyzed to recognize information from image data contained in the captured image, and an index may be generated, where the index data is based on the recognized information. Using the index, functionality such as search and retrieval is enabled. Various recognition techniques, including those that use the face, clothing, apparel, and combinations of characteristics may be utilized. Recognition may be performed on, among other things, persons and text carried on objects.

System and method for enabling the use of captured images through recognition

System and method for enabling search and retrieval from image files based on recognized information

Disclosed is an autonomous neighborhood vehicle controllable through a neighborhood social network. In one embodiment, an autonomous neighborhood vehicle can autonomously navigate to a destination specified by a user of the neighborhood social network. In one embodiment, a computer system of the autonomous neighborhood vehicle is communicatively coupled to the neighborhood social network through a wireless network to autonomously navigate to a destination specified by the user of the neighborhood social network. A navigation server provides a remote sensing capability to the autonomous neighborhood vehicle. A particular user of a third-party application is authenticated as being a verified user of the neighborhood social network having a verified residential address. An information of the particular user is communicated to the third-party application from the neighborhood social network based on a personal address privacy preference of the particular user.

Vincent Vanchoucke

Papers

System and method for recognizing objects from images and identifying relevancy amongst images and information