In this paper, we propose a segment-based object detection approach using laser range data. Our detection approach is built up of three stages: First, a hierarchical segmentation approach generates a hierarchy of coarse-to-fine segments to reduce the impact of over- and under-segmentation in later stages. Next, we employ a learned mixture model to classify all segments. The model combines multiple softmax regression classifiers learned on specific bag-of-word representations using different parameterizations of a descriptor. In the final stage, we filter irrelevant and duplicate detections using a greedy method in consideration of the segment hierarchy. We experimentally evaluate our approach on recently published real-world datasets to detect pedestrians, cars, and cyclists. © 2013 IEEE.
CITATION STYLE
Behley, J., Steinhage, V., & Cremers, A. B. (2013). Laser-based segment classification using a mixture of bag-of-words. In IEEE International Conference on Intelligent Robots and Systems (pp. 4195–4200). https://doi.org/10.1109/IROS.2013.6696957
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