{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T18:18:19Z","timestamp":1772302699646,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":97,"publisher":"ACM","license":[{"start":{"date-parts":[[2018,6,10]],"date-time":"2018-06-10T00:00:00Z","timestamp":1528588800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2018,6,10]]},"DOI":"10.1145\/3210240.3210342","type":"proceedings-article","created":{"date-parts":[[2018,7,6]],"date-time":"2018-07-06T12:36:06Z","timestamp":1530880566000},"page":"323-336","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":24,"title":["TAR"],"prefix":"10.1145","author":[{"given":"Xiaochen","family":"Liu","sequence":"first","affiliation":[{"name":"University of Southern California"}]},{"given":"Yurong","family":"Jiang","sequence":"additional","affiliation":[{"name":"LinkedIn and Hewlett-Packard Labs"}]},{"given":"Puneet","family":"Jain","sequence":"additional","affiliation":[{"name":"Google and Hewlett-Packard Labs"}]},{"given":"Kyu-Han","family":"Kim","sequence":"additional","affiliation":[{"name":"Hewlett-Packard Labs"}]}],"member":"320","published-online":{"date-parts":[[2018,6,10]]},"reference":[{"key":"e_1_3_2_2_1_1","unstructured":"3\n    Ways to Drive In-store Sales With Mobile. https:\/\/www.mobify.com\/insights\/3-ways-drive-store-sales-mobile\/.  3 Ways to Drive In-store Sales With Mobile. https:\/\/www.mobify.com\/insights\/3-ways-drive-store-sales-mobile\/."},{"key":"e_1_3_2_2_2_1","unstructured":"Facebook Location Targeting. https:\/\/www.facebook.com\/business\/a\/location-targeting.  Facebook Location Targeting. https:\/\/www.facebook.com\/business\/a\/location-targeting."},{"key":"e_1_3_2_2_3_1","unstructured":"FastDTW. https:\/\/pypi.python.org\/pypi\/fastdtw.  FastDTW. https:\/\/pypi.python.org\/pypi\/fastdtw."},{"key":"e_1_3_2_2_4_1","unstructured":"How Beacons Will Influence Billions in Us Retail Sales. http:\/\/www.businessinsider.com\/beacons-impact-billions-in-reail-sales-2015-2.  How Beacons Will Influence Billions in Us Retail Sales. http:\/\/www.businessinsider.com\/beacons-impact-billions-in-reail-sales-2015-2."},{"key":"e_1_3_2_2_5_1","unstructured":"How Nordstrom Uses Wifi To Spy On Shoppers. https:\/\/www.forbes.com\/sites\/petercohan\/2013\/05\/09\/how-nordstrom-and-home-depot-use-wifi-to-spy-on-shoppers.  How Nordstrom Uses Wifi To Spy On Shoppers. https:\/\/www.forbes.com\/sites\/petercohan\/2013\/05\/09\/how-nordstrom-and-home-depot-use-wifi-to-spy-on-shoppers."},{"key":"e_1_3_2_2_6_1","unstructured":"How Retail Stores Track You Using Your Smartphone. https:\/\/lifehacker.com\/how-retail-stores-track-you-using-your-smartphone-and-827512308.  How Retail Stores Track You Using Your Smartphone. https:\/\/lifehacker.com\/how-retail-stores-track-you-using-your-smartphone-and-827512308."},{"key":"e_1_3_2_2_7_1","unstructured":"Ios Core Bluetooth. https:\/\/developer.apple.com\/documentation\/corebluetooth.  Ios Core Bluetooth. https:\/\/developer.apple.com\/documentation\/corebluetooth."},{"key":"e_1_3_2_2_8_1","unstructured":"MTMCT on MOT Challenge. https:\/\/motchallenge.net\/data\/DukeMTMCT\/.  MTMCT on MOT Challenge. https:\/\/motchallenge.net\/data\/DukeMTMCT\/."},{"key":"e_1_3_2_2_9_1","unstructured":"Pearson Correlation Coefficient. https:\/\/en.wikipedia.org\/wiki\/Pearson_correlation_coefficient.  Pearson Correlation Coefficient. https:\/\/en.wikipedia.org\/wiki\/Pearson_correlation_coefficient."},{"key":"e_1_3_2_2_10_1","unstructured":"Spearman's Rank Correlation Coefficient. https:\/\/en.wikipedia.org\/wiki\/Spearman%27s_rank_correlation_coefficient.  Spearman's Rank Correlation Coefficient. https:\/\/en.wikipedia.org\/wiki\/Spearman%27s_rank_correlation_coefficient."},{"key":"e_1_3_2_2_11_1","unstructured":"Standard Cognition. https:\/\/www.standardcognition.com\/.  Standard Cognition. https:\/\/www.standardcognition.com\/."},{"key":"e_1_3_2_2_12_1","unstructured":"What's Mobile's Influence In-Store. https:\/\/www.marketingcharts.com\/industries\/retail-and-e-commerce-65972.  What's Mobile's Influence In-Store. https:\/\/www.marketingcharts.com\/industries\/retail-and-e-commerce-65972."},{"key":"e_1_3_2_2_13_1","unstructured":"Twitter Mobile Ads. https:\/\/business.twitter.com\/en\/advertising\/mobile-ads-companion.html 2013.  Twitter Mobile Ads. https:\/\/business.twitter.com\/en\/advertising\/mobile-ads-companion.html 2013."},{"key":"e_1_3_2_2_14_1","unstructured":"Cisco Meraki. https:\/\/meraki.cisco.com\/ 2017.  Cisco Meraki. https:\/\/meraki.cisco.com\/ 2017."},{"key":"e_1_3_2_2_15_1","unstructured":"Accenture. https:\/\/www.accenture.com 2017.  Accenture. https:\/\/www.accenture.com 2017."},{"key":"e_1_3_2_2_16_1","unstructured":"Altbeacon. http:\/\/altbeacon.org\/ 2017.  Altbeacon. http:\/\/altbeacon.org\/ 2017."},{"key":"e_1_3_2_2_17_1","unstructured":"Amazon Go. http:\/\/amazongo.com\/ 2017.  Amazon Go. http:\/\/amazongo.com\/ 2017."},{"key":"e_1_3_2_2_18_1","unstructured":"Apple IBeacon. https:\/\/developer.apple.com\/ibeacon\/ 2017.  Apple IBeacon. https:\/\/developer.apple.com\/ibeacon\/ 2017."},{"key":"e_1_3_2_2_19_1","unstructured":"Bea Inc. https:\/\/www.beainc.com\/en\/technologies\/ 2017.  Bea Inc. https:\/\/www.beainc.com\/en\/technologies\/ 2017."},{"key":"e_1_3_2_2_20_1","unstructured":"Best Advisor. https:\/\/www.bestadvisor.com\/ 2017.  Best Advisor. https:\/\/www.bestadvisor.com\/ 2017."},{"key":"e_1_3_2_2_21_1","unstructured":"Bluetooth Le: Broadcast. https:\/\/www.bluetooth.com\/what-is-bluetooth-technology\/how-it-works\/le-broadcast 2017.  Bluetooth Le: Broadcast. https:\/\/www.bluetooth.com\/what-is-bluetooth-technology\/how-it-works\/le-broadcast 2017."},{"key":"e_1_3_2_2_22_1","unstructured":"Brickstream. http:\/\/www.brickstream.com\/ 2017.  Brickstream. http:\/\/www.brickstream.com\/ 2017."},{"key":"e_1_3_2_2_23_1","unstructured":"Deloitte. https:\/\/www2.deloitte.com 2017.  Deloitte. https:\/\/www2.deloitte.com 2017."},{"key":"e_1_3_2_2_24_1","unstructured":"Eddystone Beacon. https:\/\/developers.google.com\/beacons\/ 2017.  Eddystone Beacon. https:\/\/developers.google.com\/beacons\/ 2017."},{"key":"e_1_3_2_2_25_1","unstructured":"Forrester. https:\/\/go.forrester.com\/ 2017.  Forrester. https:\/\/go.forrester.com\/ 2017."},{"key":"e_1_3_2_2_26_1","unstructured":"Hella. http:\/\/www.hella.com\/microsite-electronics\/en\/Sensors-94.html 2017.  Hella. http:\/\/www.hella.com\/microsite-electronics\/en\/Sensors-94.html 2017."},{"key":"e_1_3_2_2_27_1","unstructured":"How Beacons Can Reshape Retail Marketing. https:\/\/www.thinkwithgoogle.com\/articles\/retail-marketing-beacon-technology.html 2017.  How Beacons Can Reshape Retail Marketing. https:\/\/www.thinkwithgoogle.com\/articles\/retail-marketing-beacon-technology.html 2017."},{"key":"e_1_3_2_2_28_1","unstructured":"Irisys. http:\/\/www.irisys.net\/ 2017.  Irisys. http:\/\/www.irisys.net\/ 2017."},{"key":"e_1_3_2_2_29_1","unstructured":"Moasis. http:\/\/moasis.com\/ 2017.  Moasis. http:\/\/moasis.com\/ 2017."},{"key":"e_1_3_2_2_30_1","unstructured":"Mobile Ads. https:\/\/www.technologyreview.com\/s\/538731\/how-ads-follow-you-from-phone-to-desktop-to-tablet\/ 2017.  Mobile Ads. https:\/\/www.technologyreview.com\/s\/538731\/how-ads-follow-you-from-phone-to-desktop-to-tablet\/ 2017."},{"key":"e_1_3_2_2_31_1","unstructured":"Person Re-identification. https:\/\/github.com\/D-X-Y\/caffe-reid 2017.  Person Re-identification. https:\/\/github.com\/D-X-Y\/caffe-reid 2017."},{"key":"e_1_3_2_2_32_1","unstructured":"Point Inside. https:\/\/www.pointinside.com\/ 2017.  Point Inside. https:\/\/www.pointinside.com\/ 2017."},{"key":"e_1_3_2_2_33_1","unstructured":"Projective Transformations (homographies). http:\/\/www-prima.imag.fr\/jlc\/Courses\/2010\/ENSI3.FAI\/ENSI3.FAI.S2.EN.pdf 2017.  Projective Transformations (homographies). http:\/\/www-prima.imag.fr\/jlc\/Courses\/2010\/ENSI3.FAI\/ENSI3.FAI.S2.EN.pdf 2017."},{"key":"e_1_3_2_2_34_1","unstructured":"Shopping Easier with Store App. https:\/\/corporate.target.com\/article\/2017\/06\/sean-murphy-target-app 2017.  Shopping Easier with Store App. https:\/\/corporate.target.com\/article\/2017\/06\/sean-murphy-target-app 2017."},{"key":"e_1_3_2_2_35_1","unstructured":"Skyrec. http:\/\/www.skyrec.cc 2017.  Skyrec. http:\/\/www.skyrec.cc 2017."},{"key":"e_1_3_2_2_36_1","unstructured":"Thumbvista. https:\/\/thumbvista.com\/ 2017.  Thumbvista. https:\/\/thumbvista.com\/ 2017."},{"key":"e_1_3_2_2_37_1","unstructured":"Urban Airship. https:\/\/www.urbanairship.com\/ 2017.  Urban Airship. https:\/\/www.urbanairship.com\/ 2017."},{"key":"e_1_3_2_2_38_1","unstructured":"Xovis. https:\/\/www.xovis.com\/en\/xovis\/ 2017.  Xovis. https:\/\/www.xovis.com\/en\/xovis\/ 2017."},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"crossref","unstructured":"Bae S.-H. and Yoon K.-J. Confidence-based Data Association and Discriminative Deep Appearance Learning for Robust Online Multi-object Tracking. IEEE transactions on pattern analysis and machine intelligence 40 3 (2018) 595--610.  Bae S.-H. and Yoon K.-J. Confidence-based Data Association and Discriminative Deep Appearance Learning for Robust Online Multi-object Tracking. IEEE transactions on pattern analysis and machine intelligence 40 3 (2018) 595--610.","DOI":"10.1109\/TPAMI.2017.2691769"},{"key":"e_1_3_2_2_40_1","volume-title":"CVPR","author":"Bai S.","year":"2017"},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2011.21"},{"key":"e_1_3_2_2_42_1","first-page":"359","volume-title":"KDD workshop","author":"Berndt D. J.","year":"1994"},{"key":"e_1_3_2_2_43_1","volume-title":"CVPR","author":"Cao Z.","year":"2017"},{"key":"e_1_3_2_2_44_1","unstructured":"Chen D. Shin K. G. Jiang Y. and Kim K.-H. Locating and Tracking Ble Beacons with Smartphones.  Chen D. Shin K. G. Jiang Y. and Kim K.-H. Locating and Tracking Ble Beacons with Smartphones."},{"key":"e_1_3_2_2_45_1","first-page":"2143","volume-title":"Enhancing Detection Model for Multiple Hypothesis Tracking. In Conf. on Computer Vision and Pattern Recognition Workshops","author":"Chen J.","year":"2017"},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"crossref","unstructured":"Chen W. Cao L. Chen X. and Huang K. An Equalized Global Graph Model-based Approach for Multicamera Object Tracking. IEEE Transactions on Circuits and Systems for Video Technology 27 11 (2017) 2367--2381.  Chen W. Cao L. Chen X. and Huang K. An Equalized Global Graph Model-based Approach for Multicamera Object Tracking. IEEE Transactions on Circuits and Systems for Video Technology 27 11 (2017) 2367--2381.","DOI":"10.1109\/TCSVT.2016.2589619"},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.145"},{"key":"e_1_3_2_2_48_1","unstructured":"Chen X. and Gupta A. An Implementation of Faster Rcnn with Study for Region Sampling. arXiv preprint arXiv:1702.02138 (2017).  Chen X. and Gupta A. An Implementation of Faster Rcnn with Study for Region Sampling. arXiv preprint arXiv:1702.02138 (2017)."},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"crossref","unstructured":"De Maesschalck R. Jouan-Rimbaud D. and Massart D. L. The Mahalanobis Distance. Chemometrics and intelligent laboratory systems 50 1 (2000) 1--18.  De Maesschalck R. Jouan-Rimbaud D. and Massart D. L. The Mahalanobis Distance. Chemometrics and intelligent laboratory systems 50 1 (2000) 1--18.","DOI":"10.1016\/S0169-7439(99)00047-7"},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"crossref","unstructured":"Dehghan A. and Shah M. Binary Quadratic Programing for Online Tracking of Hundreds of People in Extremely Crowded Scenes. IEEE transactions on pattern analysis and machine intelligence 40 3 (2018) 568--581.  Dehghan A. and Shah M. Binary Quadratic Programing for Online Tracking of Hundreds of People in Extremely Crowded Scenes. IEEE transactions on pattern analysis and machine intelligence 40 3 (2018) 568--581.","DOI":"10.1109\/TPAMI.2017.2687462"},{"key":"e_1_3_2_2_51_1","unstructured":"Euclid Analytics. http:\/\/euclidanalytics.com\/ 2017.  Euclid Analytics. http:\/\/euclidanalytics.com\/ 2017."},{"key":"e_1_3_2_2_52_1","unstructured":"Everingham M. Van Gool L. Williams C. K. I. Winn J. and Zisserman A. The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results. http:\/\/www.pascal-network.org\/challenges\/VOC\/voc2012\/workshop\/index.html.  Everingham M. Van Gool L. Williams C. K. I. Winn J. and Zisserman A. The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results. http:\/\/www.pascal-network.org\/challenges\/VOC\/voc2012\/workshop\/index.html."},{"key":"e_1_3_2_2_53_1","first-page":"774","volume-title":"Improving Multi-frame Data Association with Sparse Representations for Robust Near-online Multi-object Tracking. In European Conference on Computer Vision","author":"Fagot-Bouquet L.","year":"2016"},{"key":"e_1_3_2_2_54_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2017.185"},{"key":"e_1_3_2_2_55_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.169"},{"key":"e_1_3_2_2_56_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.322"},{"key":"e_1_3_2_2_57_1","unstructured":"Henschel R. Leal-Taix\u00e9 L. Cremers D. and Rosenhahn B. Improvements to Frank-wolfe Optimization for Multi-detector Multi-object Tracking. CoRR (2017).  Henschel R. Leal-Taix\u00e9 L. Cremers D. and Rosenhahn B. Improvements to Frank-wolfe Optimization for Multi-detector Multi-object Tracking. CoRR (2017)."},{"key":"e_1_3_2_2_58_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2015.2450742"},{"key":"e_1_3_2_2_59_1","unstructured":"Inmarket. https:\/\/inmarket.com\/ 2017.  Inmarket. https:\/\/inmarket.com\/ 2017."},{"key":"e_1_3_2_2_60_1","first-page":"1338","volume-title":"Information Fusion (Fusion), 2015 18th International Conference on Information Fusion","author":"Jiang W.","year":"2015"},{"key":"e_1_3_2_2_61_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF02278710"},{"key":"e_1_3_2_2_62_1","doi-asserted-by":"publisher","DOI":"10.1145\/2994551.2994570"},{"key":"e_1_3_2_2_63_1","doi-asserted-by":"crossref","unstructured":"Kuhn H. W. The Hungarian Method for the Assignment Problem. Naval research logistics quarterly 2 1-2 (1955) 83--97.  Kuhn H. W. The Hungarian Method for the Assignment Problem. Naval research logistics quarterly 2 1-2 (1955) 83--97.","DOI":"10.1002\/nav.3800020109"},{"key":"e_1_3_2_2_64_1","doi-asserted-by":"publisher","DOI":"10.1023\/B:VISI.0000029664.99615.94"},{"key":"e_1_3_2_2_65_1","doi-asserted-by":"publisher","DOI":"10.1145\/2973750.2973754"},{"key":"e_1_3_2_2_66_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.523"},{"key":"e_1_3_2_2_67_1","first-page":"4225","volume-title":"Online Multi-target Tracking Using Recurrent Neural Networks. In AAAI","author":"Milan A.","year":"2017"},{"key":"e_1_3_2_2_68_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2016.2615538"},{"key":"e_1_3_2_2_69_1","unstructured":"BLE Proximity Technologies. http:\/\/community.silabs.com\/t5\/Official-Blog-of-Silicon-Labs\/How-to-Determine-Bluetooth-BLE-Beacon-Proximity\/ba-p\/173638 2017.  BLE Proximity Technologies. http:\/\/community.silabs.com\/t5\/Official-Blog-of-Silicon-Labs\/How-to-Determine-Bluetooth-BLE-Beacon-Proximity\/ba-p\/173638 2017."},{"key":"e_1_3_2_2_70_1","doi-asserted-by":"crossref","unstructured":"Redmon J. andFarhadi A. Yolo9000: Better Faster Stronger. arXiv preprint (2017).  Redmon J. andFarhadi A. Yolo9000: Better Faster Stronger. arXiv preprint (2017).","DOI":"10.1109\/CVPR.2017.690"},{"key":"e_1_3_2_2_71_1","first-page":"91","volume-title":"Advances in neural information processing systems","author":"Ren S.","year":"2015"},{"key":"e_1_3_2_2_72_1","unstructured":"Retail Next. https:\/\/retailnext.net\/en\/home\/ 2017.  Retail Next. https:\/\/retailnext.net\/en\/home\/ 2017."},{"key":"e_1_3_2_2_73_1","first-page":"17","volume-title":"Multi-camera Tracking. In European Conference on Computer Vision","author":"Ristani E.","year":"2016"},{"key":"e_1_3_2_2_74_1","first-page":"444","volume-title":"Tracking Multiple People Online and in Real Time. In Asian Conference on Computer Vision","author":"Ristani E.","year":"2014"},{"key":"e_1_3_2_2_75_1","doi-asserted-by":"crossref","unstructured":"Ristani E. and Tomasi C. Features for Multi-target Multi-camera Tracking and Reidentification. arXiv preprint arXiv:1803.10859 (2018).  Ristani E. and Tomasi C. Features for Multi-target Multi-camera Tracking and Reidentification. arXiv preprint arXiv:1803.10859 (2018).","DOI":"10.1109\/CVPR.2018.00632"},{"key":"e_1_3_2_2_76_1","doi-asserted-by":"publisher","DOI":"10.5555\/1367985.1367993"},{"key":"e_1_3_2_2_77_1","doi-asserted-by":"crossref","unstructured":"Schulter S. Vernaza P. Choi W. and Chandraker M. Deep Network Flow for Multi-object Tracking. arXiv preprint arXiv:1706.08482 (2017).  Schulter S. Vernaza P. Choi W. and Chandraker M. Deep Network Flow for Multi-object Tracking. arXiv preprint arXiv:1706.08482 (2017).","DOI":"10.1109\/CVPR.2017.292"},{"key":"e_1_3_2_2_78_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.210"},{"key":"e_1_3_2_2_79_1","doi-asserted-by":"publisher","DOI":"10.1145\/3131885.3131912"},{"key":"e_1_3_2_2_80_1","unstructured":"Shoppertrak. https:\/\/www.shoppertrak.com 2017.  Shoppertrak. https:\/\/www.shoppertrak.com 2017."},{"key":"e_1_3_2_2_81_1","doi-asserted-by":"publisher","DOI":"10.1145\/2789168.2790099"},{"key":"e_1_3_2_2_82_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2016.2607378"},{"key":"e_1_3_2_2_83_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2016.2607378"},{"key":"e_1_3_2_2_84_1","unstructured":"Swirl. http:\/\/www.swirl.com\/ 2017.  Swirl. http:\/\/www.swirl.com\/ 2017."},{"key":"e_1_3_2_2_85_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.394"},{"key":"e_1_3_2_2_86_1","unstructured":"Tesfaye Y. T. Zemene E. Prati A. Pelillo M. and Shah M. Multi-target Tracking in Multiple Non-overlapping Cameras using Constrained Dominant Sets. arXiv preprint arXiv:1706.06196 (2017).  Tesfaye Y. T. Zemene E. Prati A. Pelillo M. and Shah M. Multi-target Tracking in Multiple Non-overlapping Cameras using Constrained Dominant Sets. arXiv preprint arXiv:1706.06196 (2017)."},{"key":"e_1_3_2_2_87_1","volume-title":"CVPR","author":"Tran L.","year":"2017"},{"key":"e_1_3_2_2_88_1","doi-asserted-by":"crossref","unstructured":"Wojke N. Bewley A. and Paulus D. Simple Online and Realtime Tracking with a Deep Association Metric. arXiv preprint arXiv:1703.07402 (2017).  Wojke N. Bewley A. and Paulus D. Simple Online and Realtime Tracking with a Deep Association Metric. arXiv preprint arXiv:1703.07402 (2017).","DOI":"10.1109\/ICIP.2017.8296962"},{"key":"e_1_3_2_2_89_1","first-page":"4299","volume-title":"InAAAI(2017)","author":"Xu Y."},{"key":"e_1_3_2_2_90_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-015-3219-8"},{"key":"e_1_3_2_2_91_1","doi-asserted-by":"publisher","DOI":"10.1145\/2742647.2742648"},{"key":"e_1_3_2_2_92_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.103"},{"key":"e_1_3_2_2_93_1","first-page":"868","volume-title":"Mars: A Video Benchmark for Large-scale Person Re-identification. In European Conference on Computer Vision","author":"Zheng L.","year":"2016"},{"key":"e_1_3_2_2_94_1","volume-title":"IEEE International Conference on","author":"Zheng L.","year":"2015"},{"key":"e_1_3_2_2_95_1","doi-asserted-by":"publisher","DOI":"10.1145\/3159171"},{"key":"e_1_3_2_2_96_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.405"},{"key":"e_1_3_2_2_97_1","doi-asserted-by":"publisher","DOI":"10.1145\/3081333.3081335"}],"event":{"name":"MobiSys '18: The 16th Annual International Conference on Mobile Systems, Applications, and Services","location":"Munich Germany","acronym":"MobiSys '18","sponsor":["SIGMOBILE ACM Special Interest Group on Mobility of Systems, Users, Data and Computing","SIGOPS ACM Special Interest Group on Operating Systems"]},"container-title":["Proceedings of the 16th Annual International Conference on Mobile Systems, Applications, and Services"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3210240.3210342","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3210240.3210342","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T01:39:48Z","timestamp":1750210788000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3210240.3210342"}},"subtitle":["Enabling Fine-Grained Targeted Advertising in Retail Stores"],"short-title":[],"issued":{"date-parts":[[2018,6,10]]},"references-count":97,"alternative-id":["10.1145\/3210240.3210342","10.1145\/3210240"],"URL":"https:\/\/doi.org\/10.1145\/3210240.3210342","relation":{},"subject":[],"published":{"date-parts":[[2018,6,10]]},"assertion":[{"value":"2018-06-10","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}