Efficient feature tracking for scene recognition using angular and scale constraints
Recently, many vision-based robotic applications such as visual SLAM (Simultaneous Localization And Mapping) and autonomous navigation have achieved good performance using visual features. In these applications, robust feature tracking plays an important role, e.g., in scene recognition for autonomous navigation and in data association for visual SLAM. In this paper, we propose a hierarchical outlier detection algorithm for robust feature tracking; the algorithm uses a simple window-based correlation (NCC) and enforces angular and scale constraints. The proposed algorithm maximizes the inter-cluster score and detects outliers that do not satisfy the angular constraints. The remaining outliers are detected by enforcing scale constraints using SIFT descriptors. The proposed algorithm is efficient and particularly useful for scene recognition, in which an image corresponding to a query image is searched among data images. Experimental results demonstrate that the proposed algorithm is robust to outliers and image variations such as scale changes. One of the main applications of the proposed algorithm is global localization due to its low computational complexity and robustness to outliers.