Context-based object-of-interest detection for a generic traffic surveillance analysis system
We present a new traffic surveillance video analysis system, focusing on building a framework with robust and generic techniques, based on both scene understanding and moving object-of-interest detection. Since traffic surveillance is widely applied, we want to design a single system that can be reused for various traffic surveillance applications. Scene understanding provides contextual information, which improves object detection and can be further used for other applications in a traffic surveillance system. Our framework consists of two main stages: Semantic Hypothesis Generation (SHG) and Context-Based Hypothesis Verification (CBHV). In the SHG stage, a semantic region labeling engine and an appearance-based detector jointly generate the visual regions with specific features or of specific interests. The regions may also contain objects of interest, either moving or static. In the CBHV stage, a cascaded verification is performed to refine the results and smooth the detection by temporal filtering. We model the context by jointly considering spatial and scale constraints and motion saliency. Our proposed framework is validated on real-life road surveillance videos, in which objects-of-interest are moving vehicles. The results of the obtained vehicle detection outperform a recent object detection algorithm, in both precision (92.7%) and recall (92.0%). The framework is both conceptually and in the applied techniques of a generic nature and can be reused in various traffic surveillance applications, that operate, e.g. on a road crossing or in a harbor.