Learn More
This paper presents an event detection approach in crowd surveillance videos based on motion vector intersection points. It contains three steps: firstly, to extract the local motion vectors by feature tracking. Secondly, to select appropriate pairs of motion vectors and calculate three types of intersection points which represent the spatial character of(More)
We propose a novel online coregularization framework for multiview semisupervised learning based on the notion of duality in constrained optimization. Using the weak duality theorem, we reduce the online coregularization to the task of increasing the dual function. We demonstrate that the existing online coregularization algorithms in previous work can be(More)
In this paper, we propose a method to detect abnormal events using a novel unsupervised kernel learning algorithm. The key of our method is to learn a suitable feature space and the associated kernel function of the training samples. By considering the self-similarity property of training samples, we assume that the training samples will show the distinctly(More)
In this paper, we propose a dual perspective of online learning algorithm, which concerns using a window method to achieve sparsity and robustness. It makes use of Fenchel conjugates and gradient ascent to perform online learning optimization process. The window method is an update strategy for the classifier. It consists of two bounds which related to the(More)
  • 1