報告內容🧑🦯➡️：Instance-Aware Multiple Object Tracking and Segmentation (MOTS)
報告人：黃正能 教授
報告時間：11月30日 9:30
報告方式：線上（騰訊會議🚊：836 504 748）
報告人簡介:
Dr. Jenq-Neng Hwang received the BS and MS degrees, both in electrical engineering from the National Taiwan University, Taipei, Taiwan, in 1981 and 1983 separately. He then received his Ph.D. degree from the University of Southern California. In the summer of 1989, Dr. Hwang joined the Department of Electrical and Computer Engineering (ECE) of the University of Washington in Seattle, where he has been promoted to Full Professor since 1999. He served as the Associate Chair for Research from 2003 to 2005, and from 2011-2015. He also served as the Associate Chair for Global Affairs from 2015-2020. He is currently the International Programs Lead in the ECE Department. He is the founder and co-director of the Information Processing Lab., which has won several AI City Challenges awards in the past years. He has written more than 380 journal, conference papers and book chapters in the areas of machine learning, multimedia signal processing, computer vision, and multimedia system integration and networking, including an authored textbook on 'Multimedia Networking: from Theory to Practice,' published by Cambridge University Press. Dr. Hwang has close working relationship with the industry on artificial intelligence and machine learning.
Dr. Hwang received the 1995 IEEE Signal Processing Society's Best Journal Paper Award. He is a founding member of Multimedia Signal Processing Technical Committee of IEEE Signal Processing Society and was the Society's representative to IEEE Neural Network Council from 1996 to 2000. He is currently a member of Multimedia Technical Committee (MMTC) of IEEE Communication Society and also a member of Multimedia Signal Processing Technical Committee (MMSP TC) of IEEE Signal Processing Society. He served as associate editors for IEEE T-SP, T-NN and T-CSVT, T-IP and Signal Processing Magazine(SPM). He is currently on the editorial board of ZTE Communications, ETRI, IJDMB and JSPS journals. He sserved as the Program Co-Chair of IEEE ICME 2016 and was the Program Co-Chairs of ICASSP 1998 and ISCAS 2009. Dr. Hwang is a fellow of IEEE since 2001.
報告內容簡介：
Multiple object tracking (MOT) and video object segmentation (VOS) are crucial tasks in computer vision society. Further improvement and significance can be achieved by effectively combining these two tasks together, i.e., multiple object tracking and segmentation (MOTS). However, most tracking-by-detection MOT methods, with available detected bounding boxes, cannot effectively handle static, slow-moving and fast moving camera scenarios simultaneously due to ego-motion and frequent occlusion. In this work, we propose a novel tracking framework, called “instance-aware MOT” (IAMOT), that can track multiple objects in either static or moving cameras by jointly considering the instance-level features and object motions. Overall, when evaluated on the MOTS20 and KITTI-MOTS dataset, our proposed method won the first place in Track3 of the BMTT Challenge in IEEE CVPR 2020 workshop. When Lidar information is available, we further propose a multi-stage framework called “Lidar and monocular Image Fusion based multi-object Tracking and Segmentation (LIFTS)” for MOTS. This proposed framework is also evaluated on BMTT Challenge 2020 Track2: KITTI-MOTS dataset and achieves the 2nd place ranking in the competition.
