Detection of loitering individuals in public transportation areas
This paper presents a vision-based method to automatically detect individuals loitering about inner-city bus stops. Using a stationary camera view of a bus stop, pedestrians are segmented and tracked throughout the scene. The system takes snapshots of individuals when a clean, nonobstructed view of...
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| Veröffentlicht in: | IEEE transactions on intelligent transportation systems 2005-06, Vol.6 (2), p.167-177 |
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| container_title | IEEE transactions on intelligent transportation systems |
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| creator | Bird, N.D. Masoud, O. Papanikolopoulos, N.P. Isaacs, A. |
| description | This paper presents a vision-based method to automatically detect individuals loitering about inner-city bus stops. Using a stationary camera view of a bus stop, pedestrians are segmented and tracked throughout the scene. The system takes snapshots of individuals when a clean, nonobstructed view of a pedestrian is found. The snapshots are then used to classify the individual images into a database, using an appearance-based method. The features used to correlate individual images are based on short-term biometrics, which are changeable but stay valid for short periods of time; this system uses clothing color. A linear discriminant method is applied to the color information to enhance the differences and minimize similarities between the different individuals in the feature space. To determine if a given individual is loitering, time stamps collected with the snapshots in their corresponding database class can be used to judge how long an individual has been present. An experiment was performed using a 30-min video of a busy bus stop with six individuals loitering about it. Results show that the system successfully classifies images of all six individuals as loitering. |
| doi_str_mv | 10.1109/TITS.2005.848370 |
| format | Article |
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Using a stationary camera view of a bus stop, pedestrians are segmented and tracked throughout the scene. The system takes snapshots of individuals when a clean, nonobstructed view of a pedestrian is found. The snapshots are then used to classify the individual images into a database, using an appearance-based method. The features used to correlate individual images are based on short-term biometrics, which are changeable but stay valid for short periods of time; this system uses clothing color. A linear discriminant method is applied to the color information to enhance the differences and minimize similarities between the different individuals in the feature space. To determine if a given individual is loitering, time stamps collected with the snapshots in their corresponding database class can be used to judge how long an individual has been present. An experiment was performed using a 30-min video of a busy bus stop with six individuals loitering about it. Results show that the system successfully classifies images of all six individuals as loitering.</description><identifier>ISSN: 1524-9050</identifier><identifier>EISSN: 1558-0016</identifier><identifier>DOI: 10.1109/TITS.2005.848370</identifier><identifier>CODEN: ITISFG</identifier><language>eng</language><publisher>Piscataway, NJ: IEEE</publisher><subject>Analogies ; Applied sciences ; Biometrics ; Birds ; Bus stops ; Cameras ; Classification ; Color ; Computer science; control theory; systems ; Computer vision ; Control theory. 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(IEEE) 2005</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c481t-6016bd5d7256e488f52a189e1546b31bd6700cda138fdbc1a4ad61ab00a6ea603</citedby><cites>FETCH-LOGICAL-c481t-6016bd5d7256e488f52a189e1546b31bd6700cda138fdbc1a4ad61ab00a6ea603</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/1438385$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/1438385$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=16872060$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Bird, N.D.</creatorcontrib><creatorcontrib>Masoud, O.</creatorcontrib><creatorcontrib>Papanikolopoulos, N.P.</creatorcontrib><creatorcontrib>Isaacs, A.</creatorcontrib><title>Detection of loitering individuals in public transportation areas</title><title>IEEE transactions on intelligent transportation systems</title><addtitle>TITS</addtitle><description>This paper presents a vision-based method to automatically detect individuals loitering about inner-city bus stops. Using a stationary camera view of a bus stop, pedestrians are segmented and tracked throughout the scene. The system takes snapshots of individuals when a clean, nonobstructed view of a pedestrian is found. The snapshots are then used to classify the individual images into a database, using an appearance-based method. The features used to correlate individual images are based on short-term biometrics, which are changeable but stay valid for short periods of time; this system uses clothing color. A linear discriminant method is applied to the color information to enhance the differences and minimize similarities between the different individuals in the feature space. To determine if a given individual is loitering, time stamps collected with the snapshots in their corresponding database class can be used to judge how long an individual has been present. An experiment was performed using a 30-min video of a busy bus stop with six individuals loitering about it. Results show that the system successfully classifies images of all six individuals as loitering.</description><subject>Analogies</subject><subject>Applied sciences</subject><subject>Biometrics</subject><subject>Birds</subject><subject>Bus stops</subject><subject>Cameras</subject><subject>Classification</subject><subject>Color</subject><subject>Computer science; control theory; systems</subject><subject>Computer vision</subject><subject>Control theory. Systems</subject><subject>Exact sciences and technology</subject><subject>Ground, air and sea transportation, marine construction</subject><subject>human activities recognition</subject><subject>Image databases</subject><subject>Intelligent transportation systems</subject><subject>Layout</subject><subject>Monitoring</subject><subject>Pedestrians</subject><subject>Road transportation</subject><subject>Robotics</subject><subject>short-term biometrics</subject><subject>Spatial databases</subject><subject>Studies</subject><subject>Surveillance</subject><issn>1524-9050</issn><issn>1558-0016</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2005</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNqNkU1Lw0AQhoMoWKt3wUsQFC-pM9mPbI6lfhUKHqznZbPZyJY0qbuJ4L930xYKHsTTDDPPO8zMG0WXCBNEyO-X8-XbJAVgE0EFyeAoGiFjIgFAfjzkKU1yYHAanXm_ClXKEEfR9MF0Rne2beK2iuvWdsbZ5iO2TWm_bNmr2oc83vRFbXXcOdX4Tes6tVUoZ5Q_j06qQJmLfRxH70-Py9lLsnh9ns-mi0RTgV3Cwx5FycosZdxQISqWKhS5QUZ5QbAoeQagS4VEVGWhUVFVclQFgOJGcSDj6HY3d-Paz974Tq6t16auVWPa3stUiCxnPP8HCAAUWQDv_gSRZ-FLnLABvf6FrtreNeFeKXgOOUkzHiDYQdq13jtTyY2za-W-JYIcTJKDSXIwSe5MCpKb_Vzltaqr8F9t_UHHRZbC9virHWeNMYc2JYIIRn4AHQqZVg</recordid><startdate>20050601</startdate><enddate>20050601</enddate><creator>Bird, N.D.</creator><creator>Masoud, O.</creator><creator>Papanikolopoulos, N.P.</creator><creator>Isaacs, A.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Systems</topic><topic>Exact sciences and technology</topic><topic>Ground, air and sea transportation, marine construction</topic><topic>human activities recognition</topic><topic>Image databases</topic><topic>Intelligent transportation systems</topic><topic>Layout</topic><topic>Monitoring</topic><topic>Pedestrians</topic><topic>Road transportation</topic><topic>Robotics</topic><topic>short-term biometrics</topic><topic>Spatial databases</topic><topic>Studies</topic><topic>Surveillance</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bird, N.D.</creatorcontrib><creatorcontrib>Masoud, O.</creatorcontrib><creatorcontrib>Papanikolopoulos, N.P.</creatorcontrib><creatorcontrib>Isaacs, A.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Aerospace Database</collection><jtitle>IEEE transactions on intelligent transportation systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Bird, N.D.</au><au>Masoud, O.</au><au>Papanikolopoulos, N.P.</au><au>Isaacs, A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Detection of loitering individuals in public transportation areas</atitle><jtitle>IEEE transactions on intelligent transportation systems</jtitle><stitle>TITS</stitle><date>2005-06-01</date><risdate>2005</risdate><volume>6</volume><issue>2</issue><spage>167</spage><epage>177</epage><pages>167-177</pages><issn>1524-9050</issn><eissn>1558-0016</eissn><coden>ITISFG</coden><abstract>This paper presents a vision-based method to automatically detect individuals loitering about inner-city bus stops. Using a stationary camera view of a bus stop, pedestrians are segmented and tracked throughout the scene. The system takes snapshots of individuals when a clean, nonobstructed view of a pedestrian is found. The snapshots are then used to classify the individual images into a database, using an appearance-based method. The features used to correlate individual images are based on short-term biometrics, which are changeable but stay valid for short periods of time; this system uses clothing color. A linear discriminant method is applied to the color information to enhance the differences and minimize similarities between the different individuals in the feature space. To determine if a given individual is loitering, time stamps collected with the snapshots in their corresponding database class can be used to judge how long an individual has been present. An experiment was performed using a 30-min video of a busy bus stop with six individuals loitering about it. 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| source | IEEE Electronic Library (IEL) |
| subjects | Analogies Applied sciences Biometrics Birds Bus stops Cameras Classification Color Computer science control theory systems Computer vision Control theory. Systems Exact sciences and technology Ground, air and sea transportation, marine construction human activities recognition Image databases Intelligent transportation systems Layout Monitoring Pedestrians Road transportation Robotics short-term biometrics Spatial databases Studies Surveillance |
| title | Detection of loitering individuals in public transportation areas |
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