报 告 人：万少华 副教授，中南财经政法大学

邀 请 人：刘通 副教授

报告摘要：

In the Internet of Things enabled intelligent transportation systems, a huge amount of vehicle video data has been generated and real-time and accurate video analysis are very important and challenging work, especially in situations with complex street scenes. Therefore, we propose edge computing based video pre-processing to eliminate the redundant frames, so that we migrate the partial or all the video processing task to the edge, thereby diminishing the computing, storage and network bandwidth requirements of the cloud center, and enhancing the effectiveness of video analyzes. To eliminate the redundancy of the traffic video, the magnitude of motion detection based on spatio-temporal interest points (STIP) and the multi-modal linear features combination are presented which splits a video into super frame segments of interests. After that, we select the key frames from these interesting segments of the long videos with the design and detection of the prominent region. Finally, the extensive numerical experimental verification results show our methods are superior to the previous algorithms for different stages of the redundancy elimination, video segmentation, key frame selection and vehicle detection. 

报告人简介:

Shaohua Wan received the joint Ph.D. degree from School of Computer, Wuhan University and Electrical Engineering & Computer Science, Northwestern University, USA in 2010. Since then, he has been an associate professor with the School of Information and Safety Engineering, Zhongnan University of Economics and Law. From 2016 to 2017, he was a visiting professor at the Department of Electrical and Computer Engineering, Technical University of Munich, Germany. His main research interests include deep learning for Internet of Things and edge computing. He is an author of over 150 peer-reviewed research papers and books, including over 40 IEEE/ACM Transactions papers

主办单位：8188威尼斯娱人城计算机工程与科学学院