Context-driven moving object detection in aerial scenes with user input
Aerial video sequences are a common source for applications such as intelligence, surveillance or search and rescue. Their off-line analysis however requires a certain level of assistance to reduce the expert's workload. This study focuses on detecting mobile vehicles in such sequences. The proposed approach exploits two types of contextual information: loose user input as tagged areas in a reference frame, and knowledge-based priors to describe specific constraints. Our main contribution is the design of a two-step general framework able to combine these two types of information. The first step is a pixelwise semantic classification labelling each sequence frame structure in vehicle, road and background; the classifier is based on local motion and appearance features and is organized as an iterative refining process. The second step exploits knowledge-based spatial reasoning to filter out false alarms. A quantitative evaluation on real video sequences demonstrates the usefulness of each level of contextual information.