Multiview Complete Space Learning for Sparse Camera Network Research

视频监控和社交计算中的数据分析是一个问题，因为数据由多个异构特征表示。这个项目将开发一个多视图完整的空间学习框架，利用异构属性来表示从稀疏相机网络获得的图像。它将整合多种功能，以识别人员和了解行为，建立一个在广泛的监视领域发生的活动的数据库。它将扩大前沿技术，并通过提供危险（例如，过度拥挤，非法侵入），犯罪和恐怖主义情况的警告来保护澳大利亚。研究结果将在国际上适用，并增强澳大利亚在机器学习和计算机视觉领域的作用。

Data analytics in video surveillance and social computing is a problem because data are represented by multiple heterogeneous features. This project will develop a multiview complete space learning framework to exploit heterogeneous properties to represent images obtained from sparse camera networks. It will integrate multiple features to identify people and understand behaviour, to build a database of activities occurring in a wide area of surveillance. It will expand frontier technologies and safeguard Australia by providing warnings for hazardous (for example, overcrowding, trespassing), criminal, and terrorist situations. Results will be applicable internationally and enhance Australia’s role in machine learning and computer vision communities.