We present a real-time pedestrian detection system which uses cues derived from structure and appearance classification
We discuss several novel ideas to achieve computational efficien y while improving on both detection and false-alarm rates:
(i) At the front end of our system we employ stereo to detect pedestrians in 3D range maps, and to classify surrounding
structure such as buildings, trees, poles etc. in the scene. The structure classificatio efficientl labels substantial amount
of non-relevant image regions and guides the further computationally expensive process to focus on relatively small image
parts; (ii) We improve the appearance-based classifier based on HoG descriptors by performing template matching with
2D human shape contour fragments that results in improved localization and accuracy; (iii) We train individual classifier
at several depth ranges that allow us to account for appearance and 2D shape changes at variable distances in front of the
camera. Our method is evaluated on publicly available datasets and is shown to match or exceed the performance of leading
pedestrian detectors in terms of accuracy as well as achieving real-time computation (10 Hz), which makes it adequate for
deployment in fiel robots and other navigation platforms.
KEYWORDS: Clouds, Sensors, Environmental sensing, Mobile robots, Detection and tracking algorithms, LIDAR, Data processing, Data acquisition, Optical filters, Unmanned vehicles
We present an on-the-move LIDAR-based object detection system for autonomous and semi-autonomous unmanned vehicle
systems. In this paper we make several contributions: (i) we describe an algorithm for real-time detection of objects
such as doors and stairs in indoor environments; (ii) we describe efficient data structures and algorithms for processing 3D
point clouds acquired by laser scanners in a streaming manner, which minimize the memory copying and access. We show
qualitative results demonstrating the effectiveness of our approach on runs in an indoor office environment.
This paper describes a model-based 3D object recognition system, which makes use of 3D data acquired by LIDAR sensors. The system is based on a coarse-to-fine scheme for object indexing and verification to achieve high efficiency and accuracy. The system employs rotationally invariant semi-local spin image features for object representation and formulates the recognition process as a feature searching through a database, followed by a matching process guided by putative candidates between the query features and the model features. To achieve recognition efficiency with sublinear dependency on the size of the model database, an approximate nearest-neighbor method, the locality-sensitivity-hashing (LSH), is used for feature search. Geometrically constrained scene-model correspondences are used to generate alignment hypotheses that are refined during a matching and verification process for achieving high recognition accuracy. A large model database of commercial and military vehicles is used for experiments. Results on real data acquired with commercial LADAR sensor systems, mounted on either high-lift or airborne platforms are presented. Our results indicate that 3D object recognition on LADAR data is matured to the point that it is ready for real large-scale applications.
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