As any other type of data, GPS traces contain noise, anomaly, and sometimes unexpected values. Normally, researchers and data engineers analysts would start dealing with GPS data by removing those noises and outliers. However, in this work, we take the opposite direction. We focus on analyzing those unexpected values rather than discarding them. Interestingly, we discovered useful findings from an insight look at the noise in GPS trajectories. The intuition behind those discoveries is that when unexpected GPS readings are observed several times around a specific location, we study the nature of that location rather than thrown away those reading. By doing so, we are able to tell the type of area around those readings. For example, we can infer that a driver is passing by a tall building or through a forest based on the pattern of noise in the GPS readings. We are also able to question the quality of the underlying road map. Our findings and discoveries are based on the analysis of real GPS data for the Microsoft shuttles.

GPS data is noisy by nature. A typical location-based service would start by filtering out the noise from the raw GPS points that are generated by moving objects. Once the locations of the objects are identified, the location-based service is provided. In this paper, we decide not to throw away the noise. Instead, we consider the noise as an asset. We analyze the various noise patterns under different conditions and region characteristics. More specifically, we focus on one example where a lot of GPS noise is experienced; which is urban canyons. We believe that learning the GPS noise patterns in a supervised environment enables us to discover knowledge about new areas or areas where we have little knowledge. This paper is based on the analysis of GPS traces that are collected from the shuttle service within the Microsoft campuses around Seattle, Washington.

It is a fact that there are tons of GPS traces generated every minute by the millions of in-road vehicles over the world. Naturally, those traces contain imprecise readings, and most of the time they include noise and outliers. Therefore, there is a real need for a tool to allow users, companies, and researchers to get a deep insight into those raw traces and discover potential knowledge out of it. This knowledge would uncover the quality level of the GPS traces and, indeed, the quality level of the underlying map. It would also help discover interesting facts about the surrounding environment such as the type and height of buildings, the landscape cover, the weather conditions, and the nature of businesses and activities. This demo presents a system that allows users to interactively explore their collected GPS traces. Users can visually inspect the precision of their raw GPS traces, and snap these traces to the underlying road network map. Furthermore, users have the ability to clean their traces by applying various types of spatio-temporal filters. Users can perform noise analysis and produce statistics over regions of interest on the map. Last but not least, the system gives suggestions or guesses on the surrounding environment by comparing the perceived noise patterns to a database of pre-stored noise patterns. For the demo purpose, the system is initially populated with a rich data set of trajectories generated from the Microsoft shuttle service around the Greater Area of Seattle.

Video summarization is a mechanism for generating short summaries of the video to help people quickly make sense of the content of the video before downloading or seeking more detailed information. To produce reliable automatic video summarization algorithms, it is essential to first understand how human beings create video summaries with manual efforts. This paper focuses on a corpus of instructional documentary video, and seeks to improve automatic video summaries by understanding what features in the video catch the eyes and ears of human assessors, and using these findings to inform automatic summarization algorithms. The paper contributes a thorough and valuable methodology for performing automatic video summarization, and the methodology can be extended to inform summarization of other video corpuses.

Video surrogates are meant to help people quickly make sense of the content of a video before downloading or seeking more detailed information. In this paper we present the results of a study comparing the effectiveness of three different surrogates for objects in digital video libraries. Thirty-six people participated in a within subjects user study in which they did five tasks for each of three surrogate alternatives: visual alone (a storyboard), audio alone (spoken description), and combined visual and audio (a storyboard augmented with spoken description). The results show that combined surrogates are more effective, strongly preferred, and do not penalize efficiency. The results also demonstrate that spoken descriptions alone lead to better understanding of the video segments than do visual storyboards alone, although people like to have visual surrogates and use them to confirm interpretations and add context. Participants were able to easily use the combined surrogates even though they were not synchronized, suggesting that synchronization of different media channels may not be necessary in surrogates as it is in full video. The results suggest that multimodal surrogates should be incorporated into video retrieval user interfaces and audio surrogates should be used in small display interfaces. The study also raises questions about the need to synchronize different information channels in multimedia surrogates.

No abstract available.