[2019-08-15] Prof. Antoni Chan, City University of Hong Kong, "Wide-Area Crowd Counting via Ground-Plane Density Maps and Multi-View Fusion CNNs"

Poster:SHIH-YU(ERINE) PAIPost date:2019-07-29
Title: Wide-Area Crowd Counting via Ground-Plane Density Maps and Multi-View Fusion CNNs
Date: 2019-08-15 10:30am-11:20am
Location: R546, CSIE
Speaker: Prof. Antoni Chan, City University of Hong Kong
Hosted by: Prof. Yung-Yu Chuang


Crowd counting in single-view images has achieved outstanding performance on existing counting datasets. How- ever, single-view counting is not applicable to large and wide scenes (e.g., public parks, long subway platforms, or event spaces) because a single camera cannot capture the whole scene in adequate detail for counting, e.g., when the scene is too large to fit into the field-of-view of the camera, too long so that the resolution is too low on faraway crowds, or when there are too many large objects that occlude large portions of the crowd.
Therefore, to solve the wide-area counting task requires multiple cameras with overlapping fields-of-view. In this paper, we propose a deep neural network framework for multi-view crowd counting, which fuses information from multiple camera views to predict a scene- level density map on the ground-plane of the 3D world. We consider 3 versions of the fusion framework: the late fusion model fuses camera-view density maps; the naive early fusion model fuses camera-view feature maps; and the multi-view multi-scale early fusion model favors that features aligned to the same ground-plane point have consistent scales. We test our 3 fusion models on 3 multi-view counting datasets, PETS2009, DukeMTMC, and a newly collected multi-view counting dataset containing a crowded street intersection. Our methods achieve state-of-the-art results compared to other multi-view counting baselines.
Dr. Antoni Chan is an associate professor at the City University of Hong Kong in the Department of Computer Science. Before joining CityU, he was a postdoctoral researcher in the Department of Electrical and Computer Engineering at the University of California, San Diego (UC San Diego). He received the Ph.D. degree from UC San Diego in 2008 studying in the Statistical and Visual Computing Lab (SVCL). He received the B.Sc. and M.Eng. in Electrical Engineering from Cornell University in 2000 and 2001. From 2001 to 2003, he was a Visiting Scientist in the Computer Vision and Image Analysis lab at Cornell. In 2005, he was a summer intern at Google in New York City. In 2012, he was the recipient of an Early Career Award from the Research Grants Council of the Hong Kong SAR, China.
Last modification time:2019-07-29 AM 10:11

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