发表日期：2018-12-21 点击击数: 765
报告题目：Deep understanding of Urban Traffic from City Cameras
Deep understanding of urban mobility is of great significance for many real-world applications, such as urban traffic management and autonomous driving. Such problem is extremely challenging due to the low spatial and temporal resolution, high occlusion, large perspective, and variable environment conditions of the large-scale videos from the city cameras. In this talk, I will introduce my research works on extracting vehicle counts from such videos, including: 1. a deep multi-task learning framework based on fully convolutional neural networks to jointly learn vehicle density and vehicle count; 2. deep spatio-temporal networks for vehicle counting to incorporate temporal information of the traffic flow; 3. multi-source domain adaptation mechanisms with adversarial learning to adapt the deep counting model to multiple cameras; and 4. meta-learning based lifelong & few-shot vehicle counting. These techniques are organically integrated into the CityScapeEye system that are extensively evaluated and compared to existing techniques on different counting tasks and datasets, with experimental results demonstrating its effectiveness and robustness.
Dr. Shanghang Zhang recently received her PhD from Carnegie Mellon University. Her research covers computer vision and deep learning. She especially focuses on domain adaptation, meta learning, and graph convolutional networks. She has been working on traffic video analysis, salient object segmentation, multi-source domain adaptation with adversarial training, and topology adaptive graph convolutional networks, leading to publications on NIPS, CVPR, ICCV, TMM, ICLR, ICIP, ICME, etc. She is the recipient of Adobe Academic Collaboration Fund, Qualcomm Innovation Fellowship (QInF) Finalist Award, and Chiang Chen Overseas Graduate Fellowship. She has been selected to “2018 Rising Stars in EECS”, US. (a highly selected program launched at MIT in 2012, and has since been hosted at UC Berkeley, Carnegie Mellon, and Stanford annually). She was also selected to CVPR 2018 Doctoral Consortium with Travel Awards, and invited to Facebook's 3rd Annual Women in Research Lean In Event. She serves as the reviewer for journals, such as IJCV, TMM, TIP, JEI, IEEE SPL, PLOS ONE; and conferences, such as CVPR, ICCV, AAAI, ECML-PKDD, ACCV, etc. Prior to CMU, Shanghang received her Master degree from Peking University, under the supervision of Prof. Wen Gao and Prof. Xiaodong Xie. During her PhD, she interned at Adobe Research.