WCCI/IJCNN 2016 Special Session： Visual Semantic Learning from Big Surveillance
http://mc.eistar.net/VISA2016/index.htmlIntroductionIntelligent video surveillance is an important topic in the field of computer vision and pattern recognition. Significant progress has achieved in the last decades, from object detection, tracking and parsing to activity recognition and video understanding. With the development of internet technology and the ubiquitous presence of low-cost surveillance cameras nowadays, surveillance video has become a typical big data, offering both opportunities and challenges for intelligent video surveillance. On one hand, the mass data involve more abundant information to mine. On the other hand, it suffers from various difficulties such as noise, label deficiency and computational complexity. This special session focuses on learning methods to achieve high performance video analysis and understanding under uncontrolled environments in large scale, which is also a very challenging problem. Moreover, it attracts much attention from both the academia and the industry. We hope this topic will aggregate top level works on the new advances in video analysis and understanding from big surveillance data. We will solicit original contributions of researchers and practitioners from the academia as well as industry, which address a wide range of theoretical and applied issues.
TopicThe topics of interest include, but are not limited to:
[*]Deep learning for wide area surveillance applications
[*]Semi-supervised/unsupervised learning for surveillance
[*]Learning for Camera topology construction and fusion
[*]Video abstraction from mass surveillance data
[*]Activity detection/recognition/profiling from mass data
[*]Large surveillance video database annotation and searching
[*]Object retrieval/identification for surveillance
[*]Sensitive text, image/video discovery in surveillance data
[*]Analysis and understanding the videos by unmanned aerial vehicle
Paper submission deadline is on January 15, 2016
Organizers：Zhaoxiang Zhang，Xiang Bai， Rongrong Ji,and Chuanping Hu
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