The KITTI Vision Benchmark Suite

A segment-based object detection approach using laser range data only. 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 distance-dependent 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.

see paper.

@inproceedings{Behley2013IROS,
author = {Jens Behley and Volker Steinhage and Armin B. Cremers},
title = {{Laser-based Segment Classification Using a Mixture of Bag-of-Words}},
booktitle = {Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
year = {2013}}