7,417 research outputs found
Spatial Queries for Indoor Location-based Services
Indoor Location-based Services (LBS) facilitate people in indoor scenarios such as airports, train stations, shopping malls, and office buildings. Indoor spatial queries are the foundation to support indoor LBSs. However, the existing techniques for indoor spatial queries are limited to support more advanced queries that consider semantic information, temporal variations, and crowd influence. This work studies indoor spatial queries for indoor LBSs. Some typical proposals for indoor spatial queries are compared theoretically and experimentally. Then, it studies three advanced indoor spatial queries, a) Indoor Keyword-aware Routing Query. b) Indoor Temporal-variation aware Routing Query. c) Indoor Crowd-aware Routing Query. A series of techniques are proposed to solve these problems.</p
RFID-Based Indoor Spatial Query Evaluation with Bayesian Filtering Techniques
People spend a significant amount of time in indoor spaces (e.g., office
buildings, subway systems, etc.) in their daily lives. Therefore, it is
important to develop efficient indoor spatial query algorithms for supporting
various location-based applications. However, indoor spaces differ from outdoor
spaces because users have to follow the indoor floor plan for their movements.
In addition, positioning in indoor environments is mainly based on sensing
devices (e.g., RFID readers) rather than GPS devices. Consequently, we cannot
apply existing spatial query evaluation techniques devised for outdoor
environments for this new challenge. Because Bayesian filtering techniques can
be employed to estimate the state of a system that changes over time using a
sequence of noisy measurements made on the system, in this research, we propose
the Bayesian filtering-based location inference methods as the basis for
evaluating indoor spatial queries with noisy RFID raw data. Furthermore, two
novel models, indoor walking graph model and anchor point indexing model, are
created for tracking object locations in indoor environments. Based on the
inference method and tracking models, we develop innovative indoor range and k
nearest neighbor (kNN) query algorithms. We validate our solution through use
of both synthetic data and real-world data. Our experimental results show that
the proposed algorithms can evaluate indoor spatial queries effectively and
efficiently. We open-source the code, data, and floor plan at
https://github.com/DataScienceLab18/IndoorToolKit
The TREC-2002 video track report
TREC-2002 saw the second running of the Video Track, the goal of which was to promote progress in content-based retrieval from digital video via open, metrics-based evaluation. The track used 73.3 hours of publicly available digital video (in MPEG-1/VCD format) downloaded by the participants directly from the Internet Archive (Prelinger Archives) (internetarchive, 2002) and some from the Open
Video Project (Marchionini, 2001). The material comprised advertising, educational, industrial, and amateur films produced between the 1930's and the 1970's by corporations, nonprofit organizations, trade associations, community and interest groups, educational institutions, and individuals. 17 teams representing 5 companies and 12 universities - 4 from Asia, 9 from Europe, and 4 from the US - participated in one or more of three tasks in the 2001 video track: shot boundary determination, feature extraction, and search (manual or interactive). Results were scored by NIST using manually created truth data for shot boundary determination and manual assessment of feature extraction and search results. This paper is an introduction to, and an overview
of, the track framework - the tasks, data, and measures - the approaches taken by the participating groups, the results, and issues regrading the evaluation. For detailed information about the approaches and results, the reader should see the various site reports in the final workshop proceedings
Search Tracker: Human-derived object tracking in-the-wild through large-scale search and retrieval
Humans use context and scene knowledge to easily localize moving objects in
conditions of complex illumination changes, scene clutter and occlusions. In
this paper, we present a method to leverage human knowledge in the form of
annotated video libraries in a novel search and retrieval based setting to
track objects in unseen video sequences. For every video sequence, a document
that represents motion information is generated. Documents of the unseen video
are queried against the library at multiple scales to find videos with similar
motion characteristics. This provides us with coarse localization of objects in
the unseen video. We further adapt these retrieved object locations to the new
video using an efficient warping scheme. The proposed method is validated on
in-the-wild video surveillance datasets where we outperform state-of-the-art
appearance-based trackers. We also introduce a new challenging dataset with
complex object appearance changes.Comment: Under review with the IEEE Transactions on Circuits and Systems for
Video Technolog
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