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20180328-6 仉尚航:Deep Understanding of Urban Traffic from Multiple City Camera ...

2018-3-22 17:02| 发布者: 程一-计算所| 查看: 3898| 评论: 0

摘要: 报告嘉宾:仉尚航(Carnegie Mellon University)报告时间:2018年03月28日(星期三)晚上20:00(北京时间)报告题目:Deep Understanding of Urban Traffic from Multiple City Cameras主持人:杨恒(剑桥)报告摘 ...

报告嘉宾: 仉尚航(Carnegie Mellon University


报告题目:Deep Understanding of Urban Traffic from Multiple City Cameras



Deep understanding of urban traffic is of great significance for traffic management, and route planning, etc. In this talk, I introduce our research on extracting vehicle counts from streaming real-time videos captured by multiple low-resolution city cameras. The large-scale videos from the city cameras have low spatial and temporal resolution, high occlusion, large perspective, and variable environment conditions, making most existing methods lose their efficacy. To overcome these challenges, we propose the FCN-rLSTM network to jointly estimates vehicle density and vehicle count by connecting fully convolutional neural networks (FCN) with long short term memory networks (LSTM) in a residual learning fashion. Such design leverages the strengths of FCN for spatially pixel-level prediction and the strengths of LSTM for learning complex temporal dynamics. The residual learning connection reformulates the vehicle count regression as learning residual functions with reference to the sum of densities in each frame, which significantly accelerates the training of networks. To adapt the deep counting model to multiple city cameras, we further propose a new generalization bound for multi-camera domain adaptation, when there are multiple cameras with labeled instances and one target camera with unlabeled instances. Interestingly, our theory also leads to an efficient learning strategy using adversarial neural networks: we show how to interpret it as learning feature representations that are invariant to the multiple camera shifts while still being discriminative for the counting task. To this end, we propose two models: the first model directly optimizes our bound, while the second model is a smoothed approximation of the first one, leading to a more data-efficient and task-adaptive model. The optimization tasks of both models are minimax saddle point problems that can be optimized by adversarial training. To evaluate the proposed methods, we collected and labelled a large-scale traffic video dataset, containing 60 million frames from 212 webcams. Experimental results demonstrate the effectiveness and robustness of the proposed methods.


1.Understanding Traffic Density from Large-Scale Web Camera Data, Shanghang Zhang, Guanhang Wu, Joao P Costeira, José MF Moura,IEEE Conference on Computer Vision and Pattern Recognition (CVPR),2017

2.Multiple Source Domain Adaptation with Adversarial Learning, Shanghang Zhang, Han Zhao, Guanhang Wu, Joao P Costeira, José MF Moura,Geoffrey J Gordon, International Conference on Learning Representations (ICLR), Invited to Workshop, 2018.

3.FCN-rLSTM: Deep Spatio-Temporal Neural Networks for Vehicle Counting in City Cameras, Shanghang Zhang, Guanhang Wu, Joao P Costeira, José MF Moura,International Conference on Computer Vision (ICCV),2017.

4.Traffic flow from a low frame rate city camera, Evgeny Toropov, Liangyan Gui, Shanghang Zhang, Satwik Kottur, José MF Moura, IEEE International Conference on Image Processing (ICIP), 2015.


Shanghang Zhang is currently a fifth-year PhD student from Carnegie Mellon University. Her research interests include computer vision and deep learning. She has been working on vehicle detection and counting, salient object segmentation, and Image Synthesis with GAN. She is the recipient of Adobe Academic Collaboration Funding, Qualcomm Innovation Fellowship (QInF), Competition Finalist Award, and Chiang Chen Overseas Graduate Fellowship. She severs as the reviewer for PLOS ONE, CVPR, ICCV, ECML-PKDD, CIS-RAM etc. Prior to join CMU, Shanghang finished her Master study from Peking University, under the supervision of Prof. Wen Gao and Prof. Xiaodong Xie. She is also fortunate to have internship at Adobe Research, working with Xiaohui Shen, Zhe Lin, and Radomír Méch.




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