Context-based region labeling for event detection in surveillance video
Solmaz Javanbakhti, Svitlana Zinger, Peter H. N. de With · 2014
Automatic natural scene understanding and annotating regions with semantically meaningful labels, such as road or sky, are key aspects of image and video analysis. The annotation of regions is a considered helpful for improving the object-of-interest detection because the object position in the scene is also exploited. For a reliable model of a scene and associated context information, the labeling task involves image analysis at multiple, both global and local, scene levels. In this paper, we develop a general framework for performing automatic semantic labeling of video scenes by combining the local features and spatial contextual cues. While maintaining a high accuracy, we pursue an algorithm with low computational complexity, so that it is suitable for real-time implementation in embedded video surveillance. We apply our approach to a complex surveillance use case and to three different datasets: WaterVisie [1], LabelMe [2] and our own dataset. We show that our method quantitatively and qualitatively outperforms two sate-of-the-art approaches [3][4].