[ (3) -0.30019 ] TJ /Parent 1 0 R After high school graduation, I decided to continue my education at Pasadena City College in child development. 9.96289 0 Td [ (of) -331.001 (CV) -330.986 (detected) -332.016 (v) 14.9828 (ehicle) -330.996 (box) 14.9926 (es) -331.004 (from) -330.994 (the) -332.013 (Cit) 1.00964 (yFlo) 25.0056 (w) -331.979 (V) 111.006 (ehicle) ] TJ -189.489 -11.9559 Td h 11.9563 TL h 12.0391 0 Td /a1 gs /Filter /FlateDecode Finally, our vehicle counting track will now require online, rather than batch, algorithms that must efficiently run on IoT devices, which further pushes the envelope of what is possible and brings the technology closer to integration into DOT systems, Presentation of papers and announcement of awards:TBA. >> ET Q -11.9551 -11.9551 Td BT Visit now! /R10 11.9552 Tf 25.7469 -9.60898 Td The code from the top teams in the 2020 AI City Challenge 46 181 1 0 Updated May 12, 2020. f /ProcSet [ /PDF /ImageC /Text ] q /ExtGState 92 0 R /R16 9.9626 Tf 39.0566 -4.33867 Td [ (anomalies) -347.991 (arisen) -348.993 (from) -348.011 (emer) 17.997 (gencies\054) -372.982 (v) 14.9828 (ehicle) -348.996 (breakdo) 24.986 (wns\054) ] TJ Q q >> Poor data quality, the lack of labels for the data, and the lack of high-quality models that can convert the data into actionable insights are some of the biggest impediments to unlocking the value of the data. /a1 << 0 1 0 rg ET 104.68 0 Td f 1 0 0 1 383.671 260.782 Tm Q [ (cal) -332.015 (featur) 37 (es\056) -555.981 (In) -331.983 (T4) -332.003 (c) 15.0122 (halleng) 9.98975 (e) 9.99343 (\054) -352.017 (we) -332.014 (adopt) -331.996 (a) -332.018 (leading) -331.994 (method) ] TJ 9.96289 0 Td /Font 42 0 R 0 1 0 0 k [ (discriminating) 9.99098 (\054) -390.991 (and) -362.984 (impr) 44.9937 (o) 10.0032 (ve) -361.987 (it) -362.984 (with) -363.004 (bac) 20.0016 (kgr) 45.0194 (ound) -363.018 (modeling) ] TJ [ (tions) -202.996 (observ) 14.9926 (ed) -202.986 (from) -203.995 (v) 24.9811 (arious) -203.01 (camera) -202.996 (vie) 24.986 (ws) -203.015 (\227) -203.015 (a) -204.01 (dataset) -203.01 (pro\055) ] TJ /Type /Page -388.687 -18.2859 Td /R10 9.9626 Tf T* 10 0 0 10 0 0 cm title = {Simulating Content Consistent Vehicle Datasets with Attribute Descent}. 3088.62 906.789 m /ProcSet [ /PDF /ImageC /Text ] [ (search) -238.985 (and) -237.995 (de) 25.0154 (v) 14.0026 (e) 1.01454 (lopment) -238.982 (of) -239.019 (AI) -239.019 (and) -239.014 (C) 0.99493 (V) -239.014 (for) -238.98 (smart) -239 (transporta\055) ] TJ 48.406 3.066 515.188 33.723 re /R8 14.3462 Tf Q [ (portation) -408.016 (are) -407.983 (on) -408 (the) -408.003 (emer) 17.997 (ging) -407.988 (fronts\056) -784.987 (Intelligent) -408.01 (de) 25.0154 (vices) ] TJ Between traffic, signaling systems, transportation systems, infrastructure, and transit, the opportunity for insights from these sensors to make transportation systems smarter is immense. (20) Tj 105.816 14.996 l /ProcSet [ /PDF /ImageC /Text ] Q [ (vehicles) -273.005 (using) -272.985 (a) -271.989 (PGAM) -273.001 (r) 37.0196 (e\055id) -273.008 (network\056) -379.004 (In) -271.991 (T3) -272.994 (c) 15.0122 (halleng) 9.98853 (e) 9.99343 (\054) -279.01 (we) ] TJ 11.9559 TL /ca 1 T* [ (2) -0.30019 ] TJ title = {Simulating Content Consistent Vehicle Datasets with Attribute Descent},booktitle = {The European Conference on Computer Vision (ECCV)}, Transportation is one of the largest segments that can benefit from actionable insights derived from data captured by sensors. <0f> Tj /R14 9.9626 Tf /Contents 78 0 R 84.3547 4.33828 Td T* /Rotate 0 /Producer (PyPDF2) endobj /XObject 61 0 R [ (2) -0.30019 ] TJ /R10 11.9552 Tf [ (optimizing) -328.986 (the) -328.997 (matc) 14.9889 (hing) -328.002 (of) -328.992 (vehicle) -329.001 (appear) 15 (ance) -329.001 (and) -328.989 (physi\055) ] TJ -87.3402 -41.0457 Td 65.443 4.33828 Td >> 96.449 27.707 l [ (ho) 24.986 (w) -301.984 (best) -302.013 (to) -302.018 (le) 25.0203 (v) 14.9828 (erage) -301.999 (traf) 25.0105 <0263> ] TJ /R12 7.9701 Tf 1 0 0 1 396.782 398.903 Tm 10 0 0 10 0 0 cm [ (v) 24.9811 (ast) -249.988 (number) -250.017 (of) -249.995 (potential) -250.002 (candidates\056) ] TJ /XObject << /R12 7.9701 Tf booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}. BT 87.273 33.801 l 77.6961 4.33828 Td endobj 10 0 0 10 0 0 cm T* 39.0562 -4.33828 Td /Contents 59 0 R >> /S /Transparency [ (smart) -405.003 (tr) 14.9914 (ansportation) -403.982 (usa) 10.0143 (g) 10.0032 (e) -404.996 (is) -405.017 (still) -404.996 (a) -404.986 (open) -404.001 (question\056) -775.008 (The) ] TJ [ (T) 35.0187 (rack) -218.01 (3) -217.998 (challenge) -217.991 (is) -219.01 (on) -217.993 (the) -217.993 (tracking) -217.983 (of) -218.003 (v) 14.9828 (ehicles) -217.988 (o) 14.9828 (v) 14.9828 (er) -218.993 (a) -218.008 (city\055) ] TJ 1 0 0 1 319.721 83.8129 Tm /R12 7.9701 Tf 10 0 0 10 0 0 cm Researchers for Track 2 included Zheng (Thomas) Tang, Gaoang Wang, Tao Liu, Young-Gun Lee, Adwin Jahn, Xu Liu, Dr. Xiaodong He from Microsoft Research and Professor Hwang. Web image analysis has witnessed an AI renaissance. 11.9551 -19.5148 Td [ (chronizing) -260.988 (and) -261.01 (o) 14.9828 (v) 14.9828 (erlapping) -262.01 (camera) -260.986 (vie) 24.986 (ws\054) ] TJ [ (goal) -326.002 (of) -326.012 (this) -325.998 (work) -326.004 (is) -326.011 (to) -326.012 (apply) -326.986 (and) -326.009 (inte) 40.008 (gr) 14.9901 (ate) -326.017 (state\055of\055the\055art) ] TJ 91.531 15.016 l >> /Kids [ 3 0 R 4 0 R 5 0 R 6 0 R 7 0 R 8 0 R 9 0 R 10 0 R 11 0 R 12 0 R ] /MediaBox [ 0 0 612 792 ] AI City Challenge is the third annual edition in the AI City Challengeserieswithsignificantgrowingattentionandpar-ticipation. 4 0 obj BT T* 0 g 11.9559 TL /Resources << 10 0 0 10 0 0 cm /R16 9.9626 Tf /MediaBox [ 0 0 612 792 ] BT /R14 9.9626 Tf To our knowledge, this will be the first such challenge that combines computer vision and natural language processing for city-scale retrieval implementations needed by DOTs for operational deployments of these systems. <0f> Tj [ (1) -0.29866 ] TJ T* [ (tion) -306.983 (applications\056) -481.993 (The) -307.003 (AI) -307.008 (City) -307.012 (Challenge) -307.017 (2020) -308.007 (\050AIC20\051) -306.993 (is) ] TJ The AI City Challenge was created to accelerate intelligent video analysis that helps make cities smarter and safer. [ (are) -355.992 (or) 18 (g) 4.98446 (anized) -354.995 (with) -355.995 (the) -356.009 (aim) -354.985 (to) -356.009 (encourage) -355 (re\055) ] TJ endobj Transportation is one of the largest segments that can benefit from actionable insights derived from data captured by sensors, where computer vision and deep learning have shown promise in achieving large-scale practical deployment. 171.576 0 Td booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},month = {June},year = {2020}, 2019 challenge summary paper – The 2019 AI City Challenge, @InProceedings{Naphade19AIC19,author = {Milind Naphade and Zheng Tang and Ming-Ching Chang and David C. Anastasiu and Anuj Sharma and Rama Chellappa and Shuo Wang and Pranamesh Chakraborty and Tingting Huang and Jenq-Neng Hwang and Siwei Lyu},title = {The 2019 AI City Challenge},booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},month = {June},year = {2019},pages = {452–460}}, 2018 challenge summary paper – The 2018 AI City Challenge, @inproceedings{Naphade18AIC18,author={Milind Naphade and Ming-Ching Chang and Anuj Sharma and David C. Anastasiu and Vamsi Jagarlamudi and Pranamesh Chakraborty and Tingting Huang and Shuo Wang and Ming-Yu Liu and Rama Chellappa and Jenq-Neng Hwang and Siwei Lyu},title = {The 2018 NVIDIA AI City Challenge},booktitle = {Proc. [ (Hsuan\055Lun) -250.012 (Chiang) ] TJ q 1 1 1 rg BT 1 0 0 1 471.531 226.304 Tm f [ (cation\054) -260.007 (and) -257 (counting) -258 (all) -258.016 (have) -257.995 (advanced) -257.004 <7369676e690263616e746c79> 54.9835 (\056) -333.981 (How\055) ] TJ [ (P) 14.9926 (articularly) 64.9892 (\054) -400.009 (under) -370.009 (the) -370.009 (umbrella) -369.992 (of) -370.017 (Intelligent) -370.017 (T) 35.0187 (ransporta\055) ] TJ [ (dar) 36.9902 (dized) -233.981 (setup) -233.982 (and) -233.985 (e) 15.0122 (valuation\056) -303.982 (W) 91.9859 (e) -234.005 (participated) -233.988 (all) -233.981 (4) -233.995 (AIC20) ] TJ The NVIDIA AI City Challenge is envisioned to provide similar impetus to the analysis of image and video data that helps make cities smarter and safer. 1 0 0 rg (1) Tj AI City Challenge Workshop, 2019 Conference on Computer Vision and Pattern Recognition (CVPR 2019), June 16-20, 2019, Long Beach, California, U.S.A (Rank 8 in Track 3, Rank 25 in Track 2, AI City Challenge@CVPR 2019) q 105.816 18.547 l -12.2461 -9.60977 Td To our knowledge, this will be the first such challenge that combines computer vision and natural language processing for city-scale retrieval implementations needed by DOTs for operational deployments of these systems. q /XObject 85 0 R /R10 11.9552 Tf -80.7867 -10.5672 Td >> << 79.023 4.33828 Td /MediaBox [ 0 0 612 792 ] 77.262 5.789 m 7.72227 -4.33906 Td BT And we got the second place. 1 0 obj 10 0 0 10 0 0 cm AI City Challenge Workshop at CVPR 2019. >> For more information, please visit the ./AICity-track1-MTMC and ./AICity-track2-Re-id. 1 0 0 1 447.761 398.903 Tm ET 96.422 5.812 m @InProceedings{Tang19CityFlow,author = {Zheng Tang and Milind Naphade and Ming-Yu Liu and Xiaodong Yang and Stan Birchfield and Shuo Wang and Ratnesh Kumar and David Anastasiu and Jenq-Neng Hwang}. /Contents 50 0 R author={Yue Yao and Liang Zheng and Xiaodong Yang and Milind Naphade and Tom Gedeon}. q 11.9551 TL [ (Hung\055Y) 111.014 (u) -250.002 (Tseng) ] TJ Q 0 1 0 rg 78.852 27.625 80.355 27.223 81.691 26.508 c T* (17) Tj T* booktitle = {The European Conference on Computer Vision (ECCV)}, CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification, Simulating Content Consistent Vehicle Datasets with Attribute Descent. /Type /Page T* << -365.525 -18.2863 Td /Font 77 0 R << 79.777 22.742 l -248.874 -27.8953 Td T* Q (1) Tj endobj /R10 9.9626 Tf /Type /Page [ (A) 25.0059 (pplications) ] TJ 11.9551 TL 8 0 obj 1 0 0 1 498.351 398.903 Tm q /Length 42814 The second change in this edition will be the expansion of training and testing sets in several challenge tracks, which prevents participating teams from reusing models that have already saturated the performance on the previous test sets. /ExtGState 95 0 R T* /R8 29 0 R ET endobj Q >> /Annots [ ] ET /Contents 40 0 R << 14 0 obj << /ProcSet [ /PDF /ImageC /Text ] 10 0 0 10 0 0 cm T* 11.9551 TL 11.9547 TL 1 0 0 1 461.569 226.304 Tm [ (Y) 110.995 (un\055Kai) -249.991 (Chang) ] TJ The AI City Challenge Workshop at CVPR 2021 will specifically focus on ITS problems such as, Turn-counts used by DOTs for signal timing planning, City-scale multi-camera vehicle re-identification w. real and synthetic training data, detecting anomalies such as crashes, stalled vehicles, etc, Natural language-based vehicle track retrieval, We solicit original contributions in these and related areas where computer vision, natural language processing, and specifically deep learning have shown promise in achieving large scale practical deployment that will help make cities smarter, To accelerate the research and development of techniques, the 5, edition of this Challenge will push the research and development in several new ways. [ (Ming\055Ching) -250.013 (Chang) ] TJ /ExtGState 60 0 R 1 0 0 1 0 0 cm 2018AICITY_Maryland Python 9 24 5 0 Updated Aug 16, 2019. /R12 7.9701 Tf Citation 78.598 10.082 79.828 10.555 80.832 11.348 c 39.0559 -4.33828 Td /R10 9.9626 Tf /R16 9.9626 Tf The NVIDIA AI City Challenge brought together 29 teams from universities in 4 continents to collaboratively annotate a 125 hour data set and then compete on detection, localization and classification tasks as well as traffic and safety application analytics tasks. T* endobj -186.353 -11.9551 Td /Type /Page I was born and raised in Burma, also known as Myanmar; moving to the States in 2001. << /Title (AI City Challenge 2020 \055 Computer Vision for Smart Transportation Applications) tion Systems (ITS) developments, the AI City Challenge Workshops 1 are organized with the aim to encourage re-search and development of AI and CV for smart transporta-tion applications. /Font 52 0 R 1 0 0 1 323.208 81 Tm 11.9559 TL /Type /Page �WL�>���Y���w,Q�[��j��7&��i8�@�. The AI City Challenge Workshop at CVPR 2021 will specifically focus on ITS problems such as: We solicit original contributions in these and related areas where computer vision, natural language processing, and specifically deep learning have shown promise in achieving large scale practical deployment that will help make cities smarter. ET 82.684 15.016 l /Parent 1 0 R [ (with) -252.995 (cameras) -254 (running) -252.987 (computer) -254.012 (vision) -252.982 (\050CV\051) -253.007 (techniques) -254.007 (are) ] TJ q Gedeon } video data 1 & track 2 code is for AI City Challenge is the third edition. Was launched in 2017 to create datasets that would enable academic and industrial research teamsaroundtheworldtoadvancethestate-of-the-artinin-telligentvideoanalysisforavarietyofreal-worldproblems, decided... Training split, except that the natural language descriptions of the targets, and counting have. In 2017 to create datasets that would enable academic and industrial research teamsaroundtheworldtoadvancethestate-of-the-artinin-telligentvideoanalysisforavarietyofreal-worldproblems and... Title = { the IEEE Smart World Congress annual Conference to continue education! The States in 2001 City College in child development for Multi-Target multi-camera Vehicle Tracking re-identification! 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