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Pedestrian detection based on LIDAR-driven sliding window and relational parts-based detection | IEEE Conference Publication | IEEE Xplore

Pedestrian detection based on LIDAR-driven sliding window and relational parts-based detection


Abstract:

The most standard image object detectors are usually comprised of one or multiple feature extractors or classifiers within a sliding window framework. Nevertheless, this ...Show More

Abstract:

The most standard image object detectors are usually comprised of one or multiple feature extractors or classifiers within a sliding window framework. Nevertheless, this type of approach has demonstrated a very limited performance under datasets of cluttered scenes and real life situations. To tackle these issues, LIDAR space is exploited here in order to detect 2D objects in 3D space, avoiding all the inherent problems of regular sliding window techniques. Additionally, we propose a relational parts-based pedestrian detection in a probabilistic non-iid framework. With the proposed framework, we have achieved state-of-the-art performance in a pedestrian dataset gathered in a challenging urban scenario. The proposed system demonstrated superior performance in comparison with pure sliding-window-based image detectors.
Date of Conference: 23-26 June 2013
Date Added to IEEE Xplore: 15 October 2013
ISBN Information:
Print ISSN: 1931-0587
Conference Location: Gold Coast, QLD, Australia

References

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