ai city challenge

/Type /Page 1 0 0 1 447.761 398.903 Tm 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. [ (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 >> 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. (Abstract) Tj [ (mo) 10.0063 (vement) -277.991 (zone) -277.981 (matc) 14.9889 (hing) 15 (\056) -394.99 (In) -277.992 (T2) -278.012 (c) 15.0122 (halleng) 9.98853 (e) 9.99343 (\054) -285.009 (we) -279.002 (perform) -278.005 (ve\055) ] TJ [ (pr) 44.9839 (oposed) -322.003 (a) -323 (ne) 15.0183 (w) -322.011 (Multi\055Camer) 14.9865 (a) -323 (T) 54.9859 (r) 14.984 (ac) 19.9979 (king) -322.012 (Network) -323.007 (\050MTCN\051) ] TJ Q endobj /ProcSet [ /PDF /Text ] T* 87.273 33.801 l The ILSVRC benchmark has been instrumental in providing a corpus and standardized evaluation. /R12 7.9701 Tf T* /R10 9.9626 Tf -80.7867 -10.5672 Td 96.422 5.812 m BT 10 0 0 10 0 0 cm /Filter /FlateDecode /I true 1 1 1 rg (\135\056) Tj /Font 77 0 R T* 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. /Type /Group q /ExtGState 84 0 R 13 0 obj booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}. >> T* [ (c) 15.0128 (halleng) 9.98914 (e) -198.991 (t) 0.98758 (r) 14.984 (ac) 19.9979 (ks) -198.985 (\050T1) -199.01 (to) -197.994 (T4\051\056) -292.992 (In) -198.005 (T1) -199.006 (c) 15.0122 (halleng) 9.98853 (e) 9.99343 (\054) -208.982 (we) -197.996 (perform) -198.999 (ve\055) ] TJ q 10 0 0 10 0 0 cm /R10 9.9626 Tf /Length 42814 /R18 37 0 R BT << [ (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 stream 0 1 0 rg endobj -186.353 -11.9551 Td Q T* >> BT 1 0 0 1 86.3949 675.067 Tm /Type /Catalog /ExtGState << /MediaBox [ 0 0 612 792 ] Today we will unbox an opensource project of AI, called "Zero-VIRUS: Zero-shot VehIcle Route Understanding System for Intelligent Transportation". Between traffic, signaling systems, transportation systems, infrastructure, and transit, the opportunity for insights from these sensors to make transportation systems smarter is immense. 96.449 27.707 l [ (2) -0.30019 ] TJ T* >> (on\072) Tj Q [ (2) -0.30019 ] TJ ET /a0 << We serve people through free programs and classes at 7 centers and 220 community outreach locations in Southern California. 79.008 23.121 78.16 23.332 77.262 23.332 c 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. /Contents 73 0 R T* 7 0 obj /Resources 16 0 R /x6 Do 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. paper Q [ (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 /MediaBox [ 0 0 612 792 ] The AI City Challenge is jointly sponsored by IEEE and NVIDIA through the IEEE Smart World Congress annual conference. 10 0 0 10 0 0 cm The code from the top teams in the 2020 AI City Challenge 46 181 1 0 Updated May 12, 2020. /ExtGState 60 0 R /Resources << ET T* /Rotate 0 /R12 7.9701 Tf 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. 10 0 0 10 0 0 cm 9.96289 0 Td Q /a1 gs /R10 11.9552 Tf 2 0 obj <0f> Tj However, whether these technologies are ready for real-world smart transportation usage is still a open question. T* [ (Uni) 24.9957 (v) 14.9851 (ersity) -249.989 (at) -249.987 (Alban) 15.0096 (y) -250.002 (\226) -250.002 (SUNY) 128.987 (\054) -250.012 (NY) 129 (\054) -250.012 (USA) ] TJ 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. 71.715 5.789 67.215 10.68 67.215 16.707 c /ExtGState 51 0 R T* /ExtGState 79 0 R endobj -137.345 -37.8578 Td (18) Tj 11.9547 TL <0f> Tj [ (able) -380.993 (to) -380.983 (see) -380.998 (and) -382.01 (start) -380.983 (to) -380.983 (reason) -380.99 (and) -380.99 (understand) -381.99 (the) -380.985 (w) 10.0032 (orld\056) ] TJ BT /ExtGState 74 0 R >> 95.863 15.016 l /ca 1 /R10 9.9626 Tf 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. 84.3547 4.33828 Td [ (ho) 24.986 (w) -263.993 (best) -265 (to) -263.983 (perform) -263.985 (multi\055camera) -264.985 (tracking) -264.015 (on) -265.005 (syn\055) ] TJ /XObject 80 0 R stream 34.7891 TL /Contents 66 0 R [ (\135\051\054) -240.984 (tar) 17.997 (geting) ] TJ 105.816 14.996 l [ (Mask\055RCNN) -356.989 (detections) -357.009 (and) -356.004 (tr) 14.9914 (ac) 19.9979 (klets\054) -384 (followed) -356.992 (by) -357 (vehicle) ] TJ /R10 9.9626 Tf And we got the second place. 10 0 0 10 0 0 cm ET 1 0 0 1 0 0 cm 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}. q 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. /R12 7.9701 Tf [ (v) 24.9811 (ast) -249.988 (number) -250.017 (of) -249.995 (potential) -250.002 (candidates\056) ] TJ /Parent 1 0 R -9.96289 -10.5668 Td /ExtGState 95 0 R /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 ] >> [ (that) -242.014 (tak) 10.0063 (es) -241.997 (s) 0.98635 (ingle\055camer) 15 (a) -241.994 (vehicle) -241.996 (tr) 14.9914 (ac) 19.9979 (king) -241.987 (as) -241.987 (input\054) -242.986 (and) -241.984 (per) 20.004 (\055) ] TJ CVPR Workshops},pages = {53-–60}, year = 2018}, 2017 challenge summary paper – The NVIDIA AI City Challenge. 1 0 0 1 295.121 51.1121 Tm 11.9559 TL 11.9559 TL /Parent 1 0 R 0 1 0 rg 10 0 0 10 0 0 cm 3088.62 906.789 m T* 48.406 3.066 515.188 33.723 re [ (of) -202.003 (v) 14.9828 (ehicles) -201.013 (\050trucks) -201.993 (and) -201.02 (passenger) -201.996 (cars\051) -202.015 (at) -200.981 (multiple) -202 (intersec\055) ] TJ /Author (Ming\055Ching Chang\054 Chen\055Kuo Chiang\054 Chun\055Ming Tsai\054 Yun\055Kai Chang\054 Hsuan\055Lun Chiang\054 Yu\055An Wang\054 Shih\055Ya Chang\054 Yun\055Lun Li\054 Ming\055Shuin Tsai\054 Hung\055Yu Tseng) 12 0 obj >> Between traffic, signaling systems, transportation systems, infrastructure, and transit, the opportunity for insights from these sensors to make transportation systems smarter is immense. << /a1 gs /Parent 1 0 R /Font 42 0 R /R10 11.9552 Tf 9.96289 0 Td 78.059 15.016 m T* /Resources << There is a need for platforms that allow for appropriate analysis from edge to cloud, which will accelerate the development and deployment of these models. >> 11.9551 -19.5148 Td -9.96289 -10.5668 Td 1 0 0 1 319.721 83.8129 Tm 87.273 24.305 l Q [ (forms) -363.991 (multi\055camer) 15.0098 (a) -364.993 (tr) 14.9914 (ac) 19.9979 (klet) -364.012 (fusion) -365.002 (and) -364.003 (linking) 9.99098 (\054) -392.991 (by) -364.998 (jointly) ] TJ 7.72109 -4.33789 Td 0 1 0 0 k BT (17) Tj Braille Institute is an organization for the blind and visually impaired, offering rehabilitation, training and services for the visually impaired. After high school graduation, I decided to continue my education at Pasadena City College in child development. 11.9563 TL q BT 79.777 22.742 l Citation 11.9551 TL /R16 9.9626 Tf 0 1 0 rg /XObject 68 0 R [ (tion) -399.987 (Systems) -400.004 (\050ITS\051) -400.004 (de) 25.0154 (v) 14.9828 (elopments\054) -438.005 (the) -400.002 (AI) -400.012 (City) -400.017 (Challenge) ] TJ >> Q -248.874 -27.8953 Td The Challenge was inspired by the success of the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) [12] endobj 0 1 0 rg /ProcSet [ /PDF /ImageC /Text ] ET << x�t�Y��6�%��Ux��q9�T��?Њ3��$�`0&�?��W��_��_��x�z��߉��׽&�[�r��]��^��%��xAy~�6�� First, the challenge will introduce a new track for multi-camera retrieval of vehicle trajectories based on natural language descriptions of the targets. 1 0 0 1 471.531 226.304 Tm endobj /MediaBox [ 0 0 612 792 ] [ (Ming\055Shuin) -249.987 (Tsai) ] TJ /Parent 1 0 R The AI City Challenge 2020 (AIC20) is the forth sequel following the growing participation from past years (AIC17 [16], AIC18 [17], and 19 [18]), targeting T* /R10 5.9776 Tf 171.576 0 Td /Parent 1 0 R /R10 11.9552 Tf ET Submit The track 2 code is developed in Python and Pytorch. 11.9551 TL The Challenge was launched with three tracks. /R14 9.9626 Tf /R7 gs [ (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 4 0 obj 0 g 10 0 0 10 0 0 cm f 100.875 27.707 l >> /Rotate 0 /R10 9.9626 Tf [ (tec) 15.0159 (hniques) -224.992 (for) -225.003 (solving) -224.991 (the) -225.014 (c) 15.0122 (halleng) 9.98975 (e) -224.987 (pr) 44.9839 (oblems) -225 (un) -1.00964 (de) 0.98023 (r) -225.997 (a) -225.016 (stan\055) ] TJ [ (re\055identify) -340.005 (v) 14.9828 (ehicle) -340.014 (tracks) -338.997 (across) -340.017 (camera) -339.992 (vie) 24.986 (ws) -340.012 (with) -339.997 (lar) 17.997 (ge) ] TJ 3.98 w 0 g T* 82.031 6.77 79.75 5.789 77.262 5.789 c [ (AI) -249.988 (City) -250.005 (Challenge) -250.012 (2020) -249.996 (\226) -250.008 (Computer) -249.988 (V) 37.0137 (ision) -250.005 (f) 24.9923 (or) -249.995 (Smart) -250.005 (T) 73.9899 (ransportation) ] TJ [ (vided) -367.02 (by) -366.988 (the) -366.01 (Io) 25.0179 (w) 10 (a) -367.002 (Department) -367.012 (of) -366.998 (T) 35.0187 (ransportation) -366.998 (\050DO) 39.9933 (T\051\056) ] TJ 11.9563 TL 0 g AI City Challenge 2019 enabled 334 academic and industrial research teams from 44 countries to solve real-world problems using real city-scale trafﬁc camera video data. [ (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 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. /R10 11.9552 Tf 7.72109 -4.33828 Td /R14 9.9626 Tf [ (Re\055Id) -249.99 (Dataset) -249.988 (\133) ] TJ [ (3) -0.30019 ] TJ [ (AIC20) -250.013 (g) 10.0032 (ener) 15.0196 (al) -249.985 (leaderboar) 37.0098 (d\056) ] TJ T* /R12 7.9701 Tf /CA 0.5 (1) Tj /Contents 91 0 R 65.443 4.33828 Td /R14 25 0 R -5.97813 -19.957 Td Q 82.684 15.016 l [ <72652d6964656e746902636174696f6e> -229.993 (and) -229.996 (linking) -230 (of) -230 (v) 14.9828 (ehicle) -229.996 (tracklets) -229.986 (among) -230.016 (the) ] TJ /R12 7.9701 Tf /Length 17360 [ (2) -0.29866 ] TJ 105.816 18.547 l >> Q /R16 9.9626 Tf /Resources << [ (vie) 24.9836 (wing) -302.008 (v) 24.9811 (ariabilities\054) -315.006 (and) ] TJ endobj /R18 7.9701 Tf (\050iii\051) Tj T* /Resources << ET endobj /MediaBox [ 0 0 612 792 ] 39.0562 -4.33828 Td /Resources << [ (chronizing) -260.988 (and) -261.01 (o) 14.9828 (v) 14.9828 (erlapping) -262.01 (camera) -260.986 (vie) 24.986 (ws\054) ] TJ T* -388.687 -18.2859 Td -365.525 -18.2863 Td /ProcSet [ /PDF /Text ] /ProcSet [ /PDF /ImageC /Text ] ET T* /Parent 1 0 R Q -28.1461 -11.9551 Td Transportation is one of the largest segments that can benefit from actionable insights derived from data captured by sensors. [ (2) -0.30019 ] TJ author={Milind Naphade and David C. Anastasiu and Anuj Sharma and Vamsi Jagrlamudi and Hyeran Jeon and Kaikai Liu and Ming-Ching Chang and Siwei Lyu and Zeyu Gao}, Natural language-based vehicle retrieval dataset: CityFlow-NL: Tracking and Retrieval of Vehicles at City Scale by Natural Language Descriptions, @InProceedings{Feng21CityFlowNL,author={Qi Feng and Vitaly Ablavsky and Stan Sclaroff},title = {CityFlow-NL: Tracking and Retrieval of Vehicles at City Scale by Natural Language Descriptions},howpublished = {arXiv:2101.04741},year = {2021}}, Vehicle MTMC tracking & re-identification dataset – CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification. 11.9551 TL /R16 9.9626 Tf 15 0 obj 11.9551 TL 96.422 5.812 m T* 0 g >> ET endobj /R10 9.9626 Tf 2019AICITY_Code_From_Top_Teams The code from the top teams in the 2019 AI City Challenge 13 105 2 0 Updated May 11, 2020. 73.895 23.332 71.164 20.363 71.164 16.707 c /ExtGState 41 0 R [ (the) -370.99 (forth) -372.007 (sequel) -370.982 (follo) 24.9983 (wing) -371.997 (the) -370.987 (gro) 24.9811 (wing) -371.017 (participation) -372.016 (from) ] TJ 10 0 0 10 0 0 cm /Producer (PyPDF2) ET Q 109.984 9.465 l << /Resources << 2018AICity_TeamUW /Rotate 0 /Subtype /Form %PDF-1.3 (20) Tj (1) Tj [ (A) 25.0059 (pplications) ] TJ [ (T) 35.0187 (rack) -351.008 (4) -350.995 (chall) 0.99003 (enge) -350.998 (is) -350.988 (on) -350.99 (the) -350.99 (de) 0.98023 (tection) -351.015 (of) -351 (abnormal) -351.015 (traf) 25.0105 <0263> ] TJ Q (https\072\057\057www\056aicitychallenge\056org\057) Tj /R10 11.9552 Tf [ (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 BT /ProcSet [ /PDF /ImageC /Text ] /ProcSet [ /PDF /Text ] 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. [ (or) -249.995 (crashes\056) ] TJ 71.164 13.051 73.895 10.082 77.262 10.082 c [ (incidences) -348.983 (from) -348.011 (a) -349.005 (dataset) -348.986 (pro) 14.9828 (vided) -348.981 (by) -348.011 (Io) 25.0154 (w) 10.0032 (a) -349.005 (DO) 39.9982 (T) 73.9926 (\054) -348.996 (where) ] TJ /R12 7.9701 Tf 14 0 obj 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. Unfortunately, there are several reasons why these potential benefits have not yet materialized. q The AI City Challenge was created to accelerate intelligent video analysis that helps make cities smarter and safer. /R10 11.9552 Tf -11.9551 -11.9551 Td /Font 72 0 R 11.9551 TL [ (lanes) -249.988 (or) -249.995 (zones\056) ] TJ At the beginning, it was tough; my artistic side kept me focus. /Type /Page T* /R10 9.9626 Tf q BT /Font << title = {CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification}. [ (W) 79.9866 (orkshops) ] TJ 94.302 4.33828 Td T* The AI City Challenge was created to accelerate intelligent video analysis that helps make cities smarter and safer. [ (Y) 110.995 (un\055Kai) -249.991 (Chang) ] TJ /R10 11.9552 Tf S 67.215 22.738 71.715 27.625 77.262 27.625 c 11.9559 TL 11.9563 TL << /R8 14.3462 Tf >> /Rotate 0 11.9547 TL /ProcSet [ /PDF /Text ] Q 78.852 27.625 80.355 27.223 81.691 26.508 c <0f> Tj q Q Embassy Suites Brea is the only full service, all-suite hotel in Brea. 11.9551 TL The NVIDIA AI City Challenge has been created to accelerate intelligent video analysis that helps make cities smarter and safer. 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[ (\135\054) -241.019 (and) -237.995 (19) -239.009 (\133) ] TJ T* [ (tion) -341.997 (r) 37.0196 (esults) -341.994 (in) -342.989 (this) -341.996 (contest\056) -587.015 (W) 55.0117 (ith) -342.002 (the) -342.014 (blooming) -342.019 (of) -342.989 (AI) -341.982 (com\055) ] TJ /R10 9.9626 Tf 1. << /a1 << 2. T* [ (3) -0.30019 ] TJ I was born and raised in Burma, also known as Myanmar; moving to the States in 2001. /Type /Page 14.777 0 Td booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}. ET >> T* ET [ (T) 35.0187 (rack) -317.993 (1) -317.981 (challenge) -318.993 (focuses) -318.016 (on) -318.016 (the) -318.016 (counting) -317.991 (of) -319.006 (tw) 10.0081 (o) -317.981 (classes) ] TJ /R10 9.9626 Tf The AI City Challenge was ・〉st launched in 2017 to accelerate the research and develop- ment in Intelligent Transportation Systems (ITS) by pro- viding access to massive amounts of labeled data to feed learning-based algorithms. 1 0 0 1 373.708 260.782 Tm /Rotate 0 /Count 10 << [ (2) -0.30019 ] TJ /ProcSet [ /PDF /ImageC /Text ] f -11.9551 -11.9551 Td /R12 7.9701 Tf 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. 91.531 15.016 l q T* 17.9531 0 Td /ProcSet [ /PDF /Text ] 77.262 5.789 m /Rotate 0 BT /Type /Page /R10 9.9626 Tf T* [ (1) -0.29866 ] TJ [ (2) -0.30019 ] TJ 11.9551 TL 10 0 obj /R10 11.9552 Tf 7.72227 -4.33906 Td T* title = {AI City Challenge 2020 - Computer Vision for Smart Transportation Applications}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2020}} title = {Simulating Content Consistent Vehicle Datasets with Attribute Descent}. 0 g We present methods developed in our participation of the AI City 2020 Challenge (AIC20) and report evaluation results in this contest. /R10 11.9552 Tf /MediaBox [ 0 0 612 792 ] For more information, please visit the ./AICity-track1-MTMC and ./AICity-track2-Re-id. /MediaBox [ 0 0 612 792 ] T* With the blooming of AI computer vision techniques, vehicle detection, tracking, identification, and counting all have advanced significantly. [ (ho) 24.986 (w) -261.01 (best) -261 (to) ] TJ 1 0 0 rg << Q << 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. 11.9547 TL T* AI City Challenge is the third annual edition in the AI City Challengeserieswithsigniﬁcantgrowingattentionandpar-ticipation. [ (T) 35.0187 (rack) -206.992 (2) -208 (challenge) -207.012 (focuses) -207.014 (on) -207.014 (image\055based) -207.985 <72652d6964656e746902636174696f6e> ] TJ [ (e) 15.0128 (ver) 110.999 (\054) -400.009 (whether) -369.008 (these) -370.007 (tec) 15.0159 (hnolo) 9.98608 (gies) -370.003 (ar) 36.9852 (e) -369.002 (r) 37.0183 (eady) -370.014 (for) -370 (r) 37.0183 (eal\055world) ] TJ >> There should be atleast one team member. The code is for AI City Challenge 2019 Track1, MTMC Vehicle Tracking. @InProceedings{Naphade20AIC20,author = {Milind Naphade and Shuo Wang and David C. Anastasiu and Zheng Tang and Ming-Ching Chang and Xiaodong Yang and Liang Zheng and Anuj Sharma and Rama Chellappa and Pranamesh Chakraborty}. -122.633 -11.9551 Td 9.96289 0 Td 59.1063 4.33828 Td 39.0562 -4.33828 Td 6 0 obj title = {CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification},booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2019}, Synthetic 3D vehicle dataset – Simulating Content Consistent Vehicle Datasets with Attribute Descent. 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[ (Y) 110.996 (u\055An) -249.991 (W) 80 (ang) ] TJ On Saturday, May 1, from 8:30-11 a.m., the City and Republic Services will give away FREE compost to residents. AI City Challenge Workshop at CVPR 2019. /Parent 1 0 R T* The files under the frames attribute are paths in the CityFlow Benchmark used in Challenge Track 2 of the 2021 AI City Challenge. /BBox [ 0 0 612 792 ] /R10 9.9626 Tf 100.875 14.996 l /Font 52 0 R [ (2) -0.30019 ] TJ [ (past) -239.004 (years) -238 (\050AIC17) -239 (\133) ] TJ Q /Rotate 0 /R7 17 0 R [ (Among) -416.018 (man) 14.9908 (y) -416.004 (adv) 24.9824 (ancements\054) -457 (smart) -415.01 (city) -415.994 (and) -416.004 (smart) -415.992 (trans\055) ] TJ The AI City Challenge was created to accelerate intelligent video analysis that helps make cities smarter and safer. First, the challenge will introduce a new track for multi-camera retrieval of vehicle trajectories based on natural language descriptions of the targets. 10 0 0 10 0 0 cm 1 0 0 1 356.988 450.339 Tm 11.9563 TL >> T* T* 77.6961 4.33828 Td endobj 100.875 18.547 l endobj [ (in) -380.003 (recent) -380.019 (years\054) -412.989 (leading) -379.994 (to) -380.003 (a) -380.02 (f) 9.99466 (ast\055pacing) -381.015 (\223smarter) -380.01 (w) 10 (orld\224\056) ] TJ ET /Type /XObject T* 172.734 -17.9332 Td ET 39.0566 -4.33867 Td /R10 11.9552 Tf /Title (AI City Challenge 2020 \055 Computer Vision for Smart Transportation Applications) Unfortunately, there are several reasons why these potential benefits have not yet materialized. [ (Chen\055K) 15.0117 (uo) -250.012 (Chiang) ] TJ /Contents 40 0 R [ (for) -284.013 (tr) 14.9914 (af) 18.0092 <0263> -283.991 (anomaly) -284.005 (detection\056) -411.984 (W) 91.9859 (e) -283.997 (ac) 15.0183 (hie) 14.9852 (ved) -283.994 (top\0556) -284.011 (and) -284.016 (top\0554) ] TJ [ (hicle) -310.016 (counting) -309.011 (by) -309.986 (associating) -308.993 (deep) -309.986 (featur) 37.0012 (es) -309.005 (e) 19.9918 (xtr) 14.9877 (acted) -310.019 (fr) 44.9851 (om) ] TJ T* endobj q [ (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 1 0 0 1 383.671 260.782 Tm 1 0 0 -1 0 792 cm 1 0 0 1 386.82 398.903 Tm /R14 9.9626 Tf [ (\135\056) -301.009 (The) -223.997 (challenge) -223.992 (is) ] TJ /Font 65 0 R /Font 93 0 R NVIDIA AI City Challenge was launched in 2017 to create datasets that would enable academic and industrial research teamsaroundtheworldtoadvancethestate-of-the-artinin-telligentvideoanalysisforavarietyofreal-worldproblems. /Type /Page 104.68 0 Td 109.984 5.812 l 11.9559 TL [ (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 /R8 29 0 R 10 0 0 10 0 0 cm 0 g 96.3109 4.33867 Td 0 G h 0.44706 0.57647 0.77255 rg q /R10 11.9552 Tf 3 0 obj << The event will be held at the Brea Community Center parking lot and cars must enter from southbound Randolph, north of the Community Center. [ (Hung\055Y) 111.014 (u) -250.002 (Tseng) ] TJ 39.0562 -4.33867 Td With millions of traffic video cameras acting as sensors around the world, there is a significant opportunity for real-time and batch analysis of these videos to provide actionable insights. << [ (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 90.141 4.33867 Td 10 0 0 10 0 0 cm 100.875 9.465 l T* 78.598 10.082 79.828 10.555 80.832 11.348 c [ (2) -0.30019 ] TJ /R10 9.9626 Tf [ (performance) -353.99 (for) -354 (T3) -355.019 (and) -354.005 (T4) -354 (c) 15.0122 (halleng) 9.98975 (es) -354.017 (r) 37.0183 (espectively) -354.012 (in) -354.987 (the) ] TJ [ (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 >> 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. [ (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 /CA 1 /R8 11.9552 Tf >> /R12 7.9701 Tf >> >> /XObject 75 0 R [ (Y) 110.995 (un\055Lun) -249.988 (Li) ] TJ 1 0 0 1 461.569 226.304 Tm 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}. /Group << �WL�>��Y��w,Q�[��j��7&��i8�@�. endobj BT /Type /Page [ (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 10 0 0 10 0 0 cm [ (hicle) -308.017 (type) -309.004 (and) -308.012 (color) -308.989 <636c6173736902636174696f6e> -308.009 (and) -308.993 (then) -308.015 (r) 14.9828 (ank) -309.002 (matc) 14.9877 (hing) ] TJ T* << /R10 9.9626 Tf /Pages 1 0 R BT T* (\050ii\051) 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 /R10 9.9626 Tf << 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. 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