Welcome to Information Processing Lab Home Page. 

Main research thrust of our current work is in the multimedia signal processing, multimedia networking and machine learning areas. A specific focus is on the large scale smart camera networks analytics and networking. Details about various projects and research can be seen using the links on the left.

The research group works under the guidance of Prof. Jenq-Neng Hwang and Prof. Ming-Ting Sun.

Information Processing Lab

Copyright © 2015, Information Processing Laboratory, University of Washington

Our research paper, 'Camera Self-Calibration from Tracking of Moving Persons', has won two Finalist Best Student Paper Awards in Track 3 (Image, Speech, Signal and Video Processing) of the 23rd International Conference on Pattern Recognition (ICPR) sponsored by INTEL and IBM.

Our research group has achieved the top performance on the benchmark dataset, NLPR_MCT, for multi-camera object tracking (http://mct.idealtest.org/Result.html). The corresponding results are published in IEEE Transactions on Circuits and Systems for Video Technology and 2017 IEEE International Conference on Image Processing.

Updates

Our research group is the Winner of Track 2 (AI City Applications Track) at the 2017 IEEE Smart World NVIDIA AI City Challenge. 29 teams taking on this challenge hail from academic labs around the globe—Brazil, China, Greece, India, Italy, Japan, Turkey and the United States. 18 teams from 15 universities, including UC Berkeley, UIUC, SJSU, SUNY, etc., went on to the final demonstration in Fremont, California—USA. Our team is selected as the winning team for the value and innovation of our proposed approach, along with the success of our demonstration. The team members include Zheng (Thomas) Tang, Gaoang Wang, Tao Liu, Young-Gun Lee, Adwin Jahn, Xu Liu, Dr. Xiaodong He from Microsoft Research and Prof Jenq-Neng Hwang. Here is the link to the report at the official blog of NVIDIA: https://blogs.nvidia.com/blog/2017/08/09/ai-city-challenge/

Acknowledgment: The 3D CMK vehicle tracking framework is developed based on the implementation by the honorable graduates from the Information Processing Lab, Dr. Kuan-Hui Lee and Dr. Chun-Te Chu. This work also cannot be done without the assistance of our undergraduate researchers, including Lingli (Fiona) Zeng, Aotian Zheng, Yan Kuo, Kevin Nguyen, Jingwen Sun and Chien-Jen (David) Hwang.

The team from the Information Processing Lab representing the University of Washington is the winner of Track 1 (Traffic Flow Analysis) and the winner of Track 3 (Multi-camera Vehicle Detection and Reidentification ) at the AI City Challenge Workshop at CVPR 2018. There have been 22 teams from all over the world submitting there results. Our team achieves rank #1 in both of the leaderboards of Track 1 and Track 3. The team members include Zheng (Thomas) Tang, Gaoang Wang, Hao Xiao, Aotian Zheng and Prof Jenq-Neng Hwang. The published paper can be accessed here. The source code can be downloaded here.

Our story has been reported by the Department of Electrical Engineering here. The links to view our demo videos are as follows: demo of vehicle tracking and speed estimation and demo of vehicle re-identification.

Acknowledgment: We would like to thank many people who helped in the improvement of the performance of the proposed system: Chen Bai, Ge Bao, Jiarui Cai, Bill Cheung-Daihe, Tianhang Gao, Yijin Lee, Ching Lu, Shucong Ou, Ningyang Peng, Mingxin Ren, Jingwen Sun, Yi-Ting Tsai, Yun Wu, Chumei Yang, Xiao Tan, Hao Yang, and Jiacheng Zhu.