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Reliable pedestrian recognition combining high-definition LIDAR and vision data

Pedestrian recognition is one of the key technologies for advanced driver assistance systems and autonomous driving systems. The present paper proposes a fusion system for reliable pedestrian recognition using high-definition LIDAR and a vision sensor to achieve high performance under various conditions. Pedestrian candidates are extracted from two sensors in parallel by support vector machine-based classifiers. In particular, the region of interest in the image processing is set based on information about objects derived from the LIDAR processing in order to reduce false positives as well as the computational burden. All candidates are integrated by their likelihood, as calculated from their classification scores, using multiple thresholds according to the detection condition of the target in two sensors. A quantitative evaluation in a road environment confirms the effectiveness of the proposed system.