Safety for pedestrian recognition in sensor networks based on visual compressive sensing and adaptive prediction clustering
•Safety for Pedestrian recognition using sensor networks is presented.•Visual compressive sensing is presented.•Machine Learning via Adaptive Prediction Clustering is presented. Aiming at the imbalance between energy use and tracking accuracy in multi-sensor target recognition, a pedestrian target r...
Gespeichert in:
| Veröffentlicht in: | Safety science 2019-08, Vol.117, p.10-14 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 14 |
|---|---|
| container_issue | |
| container_start_page | 10 |
| container_title | Safety science |
| container_volume | 117 |
| creator | Jin, Peifen Liu, Peixue Cheng, Xiaofei |
| description | •Safety for Pedestrian recognition using sensor networks is presented.•Visual compressive sensing is presented.•Machine Learning via Adaptive Prediction Clustering is presented.
Aiming at the imbalance between energy use and tracking accuracy in multi-sensor target recognition, a pedestrian target recognition method based on visual compressed sensing and adaptive predictive clustering is proposed to track multiple pedestrians simultaneously. After acquiring the pedestrian target image, the scale invariant features of the pedestrian face in the image are extracted firstly, and the target is sparsely represented by the feature dictionary. Then adaptive prediction clustering is used to capture the change of pedestrian behavior attributes. Then, the sensor is selected by Region method, and the sensor contributing to the pedestrian area is activated to realize the pedestrian tracking. In the simulation scenario, 500 sensors are randomly deployed in a given square area. Because of fewer sensors and shorter computation time, the network lifetime has been significantly improved. |
| doi_str_mv | 10.1016/j.ssci.2019.03.025 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2264151411</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0925753519304254</els_id><sourcerecordid>2264151411</sourcerecordid><originalsourceid>FETCH-LOGICAL-c328t-8393a7ca0a44f449947b910c52e7984e8dc859d6903dd4ef1a55dd1a47a868363</originalsourceid><addsrcrecordid>eNp9kE1LAzEQhoMoWD_-gKeA513zubsBL1L8goIH9RxiMltS22RNtpXinzdtPXsayDzvTOZB6IqSmhLa3CzqnK2vGaGqJrwmTB6hCe1aVVEi2DGaEMVk1UouT9FZzgtCCOUNnaCfV9PDuMV9THgAB3lM3gScwMZ58KOPAfuAM4RcgADjd0yfGX-YDA6X3sbntVliG1dDgpz9BvasD3NsgsPGmWHcPZau83Y_zi7XeYRUkAt00ptlhsu_eo7eH-7fpk_V7OXxeXo3qyxn3Vh1XHHTWkOMEL0QSon2Q1FiJYNWdQI6ZzupXKMId05AT42UzlEjWtM1HW_4Obo-zB1S_FqXE_UirlMoKzVjjaCSCkoLxQ6UTTHnBL0ekl-ZtNWU6J1kvdA7yXonWROui-QSuj2EoPx_4yHpQkCw5dqicNQu-v_ivxjhiEg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2264151411</pqid></control><display><type>article</type><title>Safety for pedestrian recognition in sensor networks based on visual compressive sensing and adaptive prediction clustering</title><source>Elsevier ScienceDirect Journals</source><creator>Jin, Peifen ; Liu, Peixue ; Cheng, Xiaofei</creator><creatorcontrib>Jin, Peifen ; Liu, Peixue ; Cheng, Xiaofei</creatorcontrib><description>•Safety for Pedestrian recognition using sensor networks is presented.•Visual compressive sensing is presented.•Machine Learning via Adaptive Prediction Clustering is presented.
Aiming at the imbalance between energy use and tracking accuracy in multi-sensor target recognition, a pedestrian target recognition method based on visual compressed sensing and adaptive predictive clustering is proposed to track multiple pedestrians simultaneously. After acquiring the pedestrian target image, the scale invariant features of the pedestrian face in the image are extracted firstly, and the target is sparsely represented by the feature dictionary. Then adaptive prediction clustering is used to capture the change of pedestrian behavior attributes. Then, the sensor is selected by Region method, and the sensor contributing to the pedestrian area is activated to realize the pedestrian tracking. In the simulation scenario, 500 sensors are randomly deployed in a given square area. Because of fewer sensors and shorter computation time, the network lifetime has been significantly improved.</description><identifier>ISSN: 0925-7535</identifier><identifier>EISSN: 1879-1042</identifier><identifier>DOI: 10.1016/j.ssci.2019.03.025</identifier><language>eng</language><publisher>Amsterdam: Elsevier Ltd</publisher><subject>Adaptive prediction clustering ; Clustering ; Computer simulation ; Energy consumption ; Feature extraction ; Image acquisition ; Pedestrian areas ; Pedestrian safety ; Pedestrians ; Predictions ; Safety ; Sensors ; Simulation ; Target acquisition ; Target recognition ; Tracking ; Visual compressed sensing ; Wireless sensor networks</subject><ispartof>Safety science, 2019-08, Vol.117, p.10-14</ispartof><rights>2019 Elsevier Ltd</rights><rights>Copyright Elsevier BV Aug 2019</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c328t-8393a7ca0a44f449947b910c52e7984e8dc859d6903dd4ef1a55dd1a47a868363</citedby><cites>FETCH-LOGICAL-c328t-8393a7ca0a44f449947b910c52e7984e8dc859d6903dd4ef1a55dd1a47a868363</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0925753519304254$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65534</link.rule.ids></links><search><creatorcontrib>Jin, Peifen</creatorcontrib><creatorcontrib>Liu, Peixue</creatorcontrib><creatorcontrib>Cheng, Xiaofei</creatorcontrib><title>Safety for pedestrian recognition in sensor networks based on visual compressive sensing and adaptive prediction clustering</title><title>Safety science</title><description>•Safety for Pedestrian recognition using sensor networks is presented.•Visual compressive sensing is presented.•Machine Learning via Adaptive Prediction Clustering is presented.
Aiming at the imbalance between energy use and tracking accuracy in multi-sensor target recognition, a pedestrian target recognition method based on visual compressed sensing and adaptive predictive clustering is proposed to track multiple pedestrians simultaneously. After acquiring the pedestrian target image, the scale invariant features of the pedestrian face in the image are extracted firstly, and the target is sparsely represented by the feature dictionary. Then adaptive prediction clustering is used to capture the change of pedestrian behavior attributes. Then, the sensor is selected by Region method, and the sensor contributing to the pedestrian area is activated to realize the pedestrian tracking. In the simulation scenario, 500 sensors are randomly deployed in a given square area. Because of fewer sensors and shorter computation time, the network lifetime has been significantly improved.</description><subject>Adaptive prediction clustering</subject><subject>Clustering</subject><subject>Computer simulation</subject><subject>Energy consumption</subject><subject>Feature extraction</subject><subject>Image acquisition</subject><subject>Pedestrian areas</subject><subject>Pedestrian safety</subject><subject>Pedestrians</subject><subject>Predictions</subject><subject>Safety</subject><subject>Sensors</subject><subject>Simulation</subject><subject>Target acquisition</subject><subject>Target recognition</subject><subject>Tracking</subject><subject>Visual compressed sensing</subject><subject>Wireless sensor networks</subject><issn>0925-7535</issn><issn>1879-1042</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNp9kE1LAzEQhoMoWD_-gKeA513zubsBL1L8goIH9RxiMltS22RNtpXinzdtPXsayDzvTOZB6IqSmhLa3CzqnK2vGaGqJrwmTB6hCe1aVVEi2DGaEMVk1UouT9FZzgtCCOUNnaCfV9PDuMV9THgAB3lM3gScwMZ58KOPAfuAM4RcgADjd0yfGX-YDA6X3sbntVliG1dDgpz9BvasD3NsgsPGmWHcPZau83Y_zi7XeYRUkAt00ptlhsu_eo7eH-7fpk_V7OXxeXo3qyxn3Vh1XHHTWkOMEL0QSon2Q1FiJYNWdQI6ZzupXKMId05AT42UzlEjWtM1HW_4Obo-zB1S_FqXE_UirlMoKzVjjaCSCkoLxQ6UTTHnBL0ekl-ZtNWU6J1kvdA7yXonWROui-QSuj2EoPx_4yHpQkCw5dqicNQu-v_ivxjhiEg</recordid><startdate>201908</startdate><enddate>201908</enddate><creator>Jin, Peifen</creator><creator>Liu, Peixue</creator><creator>Cheng, Xiaofei</creator><general>Elsevier Ltd</general><general>Elsevier BV</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QQ</scope><scope>7SC</scope><scope>7SE</scope><scope>7SP</scope><scope>7SR</scope><scope>7T2</scope><scope>7TA</scope><scope>7TB</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>C1K</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>JG9</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>NAPCQ</scope></search><sort><creationdate>201908</creationdate><title>Safety for pedestrian recognition in sensor networks based on visual compressive sensing and adaptive prediction clustering</title><author>Jin, Peifen ; Liu, Peixue ; Cheng, Xiaofei</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c328t-8393a7ca0a44f449947b910c52e7984e8dc859d6903dd4ef1a55dd1a47a868363</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Adaptive prediction clustering</topic><topic>Clustering</topic><topic>Computer simulation</topic><topic>Energy consumption</topic><topic>Feature extraction</topic><topic>Image acquisition</topic><topic>Pedestrian areas</topic><topic>Pedestrian safety</topic><topic>Pedestrians</topic><topic>Predictions</topic><topic>Safety</topic><topic>Sensors</topic><topic>Simulation</topic><topic>Target acquisition</topic><topic>Target recognition</topic><topic>Tracking</topic><topic>Visual compressed sensing</topic><topic>Wireless sensor networks</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jin, Peifen</creatorcontrib><creatorcontrib>Liu, Peixue</creatorcontrib><creatorcontrib>Cheng, Xiaofei</creatorcontrib><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Health and Safety Science Abstracts (Full archive)</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>Materials 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>Nursing & Allied Health Premium</collection><jtitle>Safety science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jin, Peifen</au><au>Liu, Peixue</au><au>Cheng, Xiaofei</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Safety for pedestrian recognition in sensor networks based on visual compressive sensing and adaptive prediction clustering</atitle><jtitle>Safety science</jtitle><date>2019-08</date><risdate>2019</risdate><volume>117</volume><spage>10</spage><epage>14</epage><pages>10-14</pages><issn>0925-7535</issn><eissn>1879-1042</eissn><abstract>•Safety for Pedestrian recognition using sensor networks is presented.•Visual compressive sensing is presented.•Machine Learning via Adaptive Prediction Clustering is presented.
Aiming at the imbalance between energy use and tracking accuracy in multi-sensor target recognition, a pedestrian target recognition method based on visual compressed sensing and adaptive predictive clustering is proposed to track multiple pedestrians simultaneously. After acquiring the pedestrian target image, the scale invariant features of the pedestrian face in the image are extracted firstly, and the target is sparsely represented by the feature dictionary. Then adaptive prediction clustering is used to capture the change of pedestrian behavior attributes. Then, the sensor is selected by Region method, and the sensor contributing to the pedestrian area is activated to realize the pedestrian tracking. In the simulation scenario, 500 sensors are randomly deployed in a given square area. Because of fewer sensors and shorter computation time, the network lifetime has been significantly improved.</abstract><cop>Amsterdam</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.ssci.2019.03.025</doi><tpages>5</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0925-7535 |
| ispartof | Safety science, 2019-08, Vol.117, p.10-14 |
| issn | 0925-7535 1879-1042 |
| language | eng |
| recordid | cdi_proquest_journals_2264151411 |
| source | Elsevier ScienceDirect Journals |
| subjects | Adaptive prediction clustering Clustering Computer simulation Energy consumption Feature extraction Image acquisition Pedestrian areas Pedestrian safety Pedestrians Predictions Safety Sensors Simulation Target acquisition Target recognition Tracking Visual compressed sensing Wireless sensor networks |
| title | Safety for pedestrian recognition in sensor networks based on visual compressive sensing and adaptive prediction clustering |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-19T23%3A16%3A41IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Safety%20for%20pedestrian%20recognition%20in%20sensor%20networks%20based%20on%20visual%20compressive%20sensing%20and%20adaptive%20prediction%20clustering&rft.jtitle=Safety%20science&rft.au=Jin,%20Peifen&rft.date=2019-08&rft.volume=117&rft.spage=10&rft.epage=14&rft.pages=10-14&rft.issn=0925-7535&rft.eissn=1879-1042&rft_id=info:doi/10.1016/j.ssci.2019.03.025&rft_dat=%3Cproquest_cross%3E2264151411%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2264151411&rft_id=info:pmid/&rft_els_id=S0925753519304254&rfr_iscdi=true |