Citation

Oliveira L, Nunes U, Peixoto P, Silva M, Moita F. Pattern Recognit. 2010; 43(10): 3648-3659.

Copyright

(Copyright © 2010, Elsevier Publishing)

DOI

10.1016/j.patcog.2010.05.014

PMID

unavailable

Abstract

Fusion of laser and vision in object detection has been accomplished by two main approaches: (1) independent integration of sensor-driven features or sensor-driven classifiers, or (2) a region of interest (ROI) is found by laser segmentation and an image classifier is used to name the projected ROI. Here, we propose a novel fusion approach based on semantic information, and embodied on many levels. Sensor fusion is based on spatial relationship of parts-based classifiers, being performed via a Markov logic network. The proposed system deals with partial segments, it is able to recover depth information even if the laser fails, and the integration is modeled through contextual information--characteristics not found on previous approaches. Experiments in pedestrian detection demonstrate the effectiveness of our method over data sets gathered in urban scenarios.