LIDAR-based object detection usually relies on geometric feature extraction, followed by a generative or discriminative classification approach. Instead, we propose to change the way of detecting objects using LIDAR by means of not only a featureless approach, but also inferring context-aware relations of object parts. For the first feature, a coarse-to-fine segmentation based on β-skeleton random graph is proposed; after segmentation, each segment is labeled, and scored by a Procrustes analysis. For the second feature, after defining the sub-segments of each object, a contextual analysis is in charge of assessing levels of intra-object or inter-object relationship, ultimately integrated into a Markov logic network. This way, we contribute with a system which deals with partial segmentation, also embodying contextual information. The system proof-of-concept is in pedestrian detection, but the rationale of the approach can be applied to any other object after the definition of its physical structure. The effectiveness of the proposed method was assessed over a data set gathered in challenging scenarios, with a significant gain in accuracy over a full segmentation version of the system.
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