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Jenq-Neng Hwang

  • Professor
  • Associate Chair for Global Affairs and International Development

Jenq-Neng Hwang received his bachelor’s and master’s degrees, both in electrical engineering, from the National Taiwan University, Taipei, Taiwan, in 1981 and 1983 respectively. He received his Ph.D. from the University of Southern California in 1988. In 1989, Hwang joined UW EE, becoming a full professor in 1999. He served as the Associate Chair for Research from 2003-2005 and again from 2011-2015. Hwang is currently the Associate Chair for Global Affairs and International Development. He has written more than 300 journal articles, conference papers and book chapters in the areas of multimedia signal processing and multimedia system integration and networking. He is the author of the textbook “Multimedia Networking: from Theory to Practice.” Hwang has a close working relationship with industry on multimedia signal processing and multimedia networking.

Research Interests

Multimedia signal processing, pattern recognition, machine learning, multimedia networking.

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                    [post_content] => [caption id="attachment_11173" align="alignleft" width="506"] The UW EE team at the AI City Challenge.[/caption]

A team of graduate students and researchers, led by UW Electrical Engineering (UW EE) Professor Jenq-Neng Hwang won the Track 2 challenge in the IEEE Smart World NVIDIA AI City Challenge.

Estimations assert that there will be 1 billion cameras on the road by 2020. These cameras present a significant opportunity for transportation; their data can offer actionable insights to make transportation systems smarter and safer. However, current systems present challenges. Poor data quality, a 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 blockers to harnessing the data's value.

The AI City Challenge requested research proposals for two tracks to address these problems. For Track 1, researchers were asked to develop models for basic machine learning tasks as they applied to current transportation systems' data. Track 2 researchers were asked to propose and develop AI City applications geared towards solving salient problems related to safety and/or congestion in an urban environment.

The team of researchers in Professor Hwang's Information Processing Lab won the Track 2 challenge for AI City Applications.  Their work focused on a constrained multiple-kernel (CMK) tracking system to resolve the problem of occlusion during multiple object tracking. This model also enables researchers to understand vehicle attributes, like vehicle type, speed, orientation, etc. The researchers also proposed several future improvements on their current work, including license plate identification and application in multiple-camera tracking. Their experiments on the NVIDIA dataset outperformed several state-of-the-art algorithms in tracking by segmentation and tracking by detection.

The AI City Challenge is jointly sponsored by IEEE and NVIDIA through the IEEE Smart World Congress annual conference. 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.

Team lead Zheng Tang urged that this win was a team effort, thanking the UW undergraduate students, who offered assistance: Lingli Zeng, Aotian Zheng, Yan Kuo, Kevin Nguyen, Jingwen Sun and Chien-Jen Hwang.

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Challenge.[/caption] A team of graduate students and researchers, led by UW Electrical Engineering (UW EE) Professor Jenq-Neng Hwang won the Track 2 challenge in the IEEE Smart World NVIDIA AI City Challenge. Estimations assert that there will be 1 billion cameras on the road by 2020. These cameras present a significant opportunity for transportation; their data can offer actionable insights to make transportation systems smarter and safer. However, current systems present challenges. Poor data quality, a 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 blockers to harnessing the data's value. The AI City Challenge requested research proposals for two tracks to address these problems. For Track 1, researchers were asked to develop models for basic machine learning tasks as they applied to current transportation systems' data. Track 2 researchers were asked to propose and develop AI City applications geared towards solving salient problems related to safety and/or congestion in an urban environment. The team of researchers in Professor Hwang's Information Processing Lab won the Track 2 challenge for AI City Applications.  Their work focused on a constrained multiple-kernel (CMK) tracking system to resolve the problem of occlusion during multiple object tracking. This model also enables researchers to understand vehicle attributes, like vehicle type, speed, orientation, etc. The researchers also proposed several future improvements on their current work, including license plate identification and application in multiple-camera tracking. Their experiments on the NVIDIA dataset outperformed several state-of-the-art algorithms in tracking by segmentation and tracking by detection. The AI City Challenge is jointly sponsored by IEEE and NVIDIA through the IEEE Smart World Congress annual conference. 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. Team lead Zheng Tang urged that this win was a team effort, thanking the UW undergraduate students, who offered assistance: Lingli Zeng, Aotian Zheng, Yan Kuo, Kevin Nguyen, Jingwen Sun and Chien-Jen Hwang.

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See More: NVIDIA blog post [post_title] => UW EE-based team wins AI City Challenge [post_excerpt] => [post_status] => publish [comment_status] => closed [ping_status] => closed [post_password] => [post_name] => uw-ee-based-team-wins-ai-city-challenge [to_ping] => [pinged] => [post_modified] => 2017-08-10 09:59:20 [post_modified_gmt] => 2017-08-10 16:59:20 [post_content_filtered] => [post_parent] => 0 [guid] => http://www.ee.washington.edu/?post_type=spotlight&p=11171 [menu_order] => 15 [post_type] => spotlight [post_mime_type] => [comment_count] => 0 [filter] => raw ) ) [post_count] => 1 [current_post] => -1 [in_the_loop] => [post] => WP_Post Object ( [ID] => 11171 [post_author] => 12 [post_date] => 2017-08-09 16:31:38 [post_date_gmt] => 2017-08-09 23:31:38 [post_content] => [caption id="attachment_11173" align="alignleft" width="506"] The UW EE team at the AI City Challenge.[/caption] A team of graduate students and researchers, led by UW Electrical Engineering (UW EE) Professor Jenq-Neng Hwang won the Track 2 challenge in the IEEE Smart World NVIDIA AI City Challenge. Estimations assert that there will be 1 billion cameras on the road by 2020. These cameras present a significant opportunity for transportation; their data can offer actionable insights to make transportation systems smarter and safer. However, current systems present challenges. Poor data quality, a 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 blockers to harnessing the data's value. The AI City Challenge requested research proposals for two tracks to address these problems. For Track 1, researchers were asked to develop models for basic machine learning tasks as they applied to current transportation systems' data. Track 2 researchers were asked to propose and develop AI City applications geared towards solving salient problems related to safety and/or congestion in an urban environment. The team of researchers in Professor Hwang's Information Processing Lab won the Track 2 challenge for AI City Applications.  Their work focused on a constrained multiple-kernel (CMK) tracking system to resolve the problem of occlusion during multiple object tracking. This model also enables researchers to understand vehicle attributes, like vehicle type, speed, orientation, etc. The researchers also proposed several future improvements on their current work, including license plate identification and application in multiple-camera tracking. Their experiments on the NVIDIA dataset outperformed several state-of-the-art algorithms in tracking by segmentation and tracking by detection. The AI City Challenge is jointly sponsored by IEEE and NVIDIA through the IEEE Smart World Congress annual conference. 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. Team lead Zheng Tang urged that this win was a team effort, thanking the UW undergraduate students, who offered assistance: Lingli Zeng, Aotian Zheng, Yan Kuo, Kevin Nguyen, Jingwen Sun and Chien-Jen Hwang.

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Representative Publications

  • Kuan-Hui Lee, Jenq-Neng Hwang, “On-Road Pedestrian Tracking across Multiple Driving Recorders, ” IEEE Trans. on Multimedia, special issue on Multimedia: the Biggest Big Data, 17(9):1429-1438, Sept. 2015.
  • Xiang Chen, Jenq-Neng Hwang, Chung-Nan Lee, Shih-I Chen “A Near Optimal QoE-Driven Power Allocation Scheme for Scalable Video Transmissions over MIMO Systems, ” IEEE Journal of Selected Topics in Signal Processing, Special Issue on Visual Signal Processing for Wireless Networks, 9(1):76-88, Feb. 2015.
  • Meng-Che Chuang, Jenq-Neng Hwang, Kresimir Williams, Richard Towler “Tracking Live Fish from Low-Contrast and Low-Frame-Rate Stereo Videos, ” IEEE Trans. on Circuits and Systems for Video Technologies (CSVT), 25(1):167-179, Jan. 2015.
  • Kuan-Hui Lee, Jenq-Neng Hwang, Shihi Chen “Model-Based Vehicle Localization Based on Three-Dimensional Constrained Multiple-Kernel Tracking, ” IEEE Trans. on Circuits and Systems for Video Technologies (CSVT), 25(1)38-50, Jan. 2015.
  • Chun-Te Chu and Jenq-Neng Hwang, “Fully Unsupervised Learning of Camera Link Models for Tracking Humans Across Non-overlapping Cameras,” IEEE Trans. on Circuits and Systems for Video Technologies (CSVT), 24(6):979-994, June 2014.
  • Chun-Te Chu and Jenq-Neng Hwang, Hung-I Pai, Kung-Ming Lan “Tracking Human Under Occlusion Based On Adaptive Multiple Kernels With Projected Gradients,” IEEE Trans. on Multimedia, 15(7):1602-1615, November 2013.
Jenq-Neng Hwang Headshot
Phone206-685-1603
hwang@uw.edu
Web PageClick Here
Mail
M426 EEB

Associated Labs

Research Areas

Affiliations

Innovation/Entrepreneurship

  • Moving Cameras Talk to Each Other to Identify and Track Pedestrians
  • On-Road Pedestrian Tracking across Multiple Driving Recorders
  • Video Analyses for Fishery Sciences

Education

  • Ph.D., Electrical Engineering, 1988
    University of Southern California
  • MS, Electrical Engineering, 1983
    National Taiwan University
  • BS, Electrical Engineering, 1981
    National Taiwan University