Abstract
This paper presents two different modules for the validation of human shape presence in far-infrared images. These modules are part of a more complex system aimed at the detection of pedestrians by means of the simultaneous use of two stereo vision systems in both far-infrared and daylight domains. The first module detects the presence of a human shape in a list of areas of attention using active contours to detect the object shape and evaluating the results by means of a neural network. The second validation subsystem directly exploits a neural network for each area of attention in the far-infrared images and produces a list of votes.
Highlights
During the last years, pedestrian detection has been a key topic of the research on intelligent vehicles
The developed system has been tested in different situations using an experimental vehicle equipped with the tetra-vision system
The discussion will focus on results given by both neural networks, one working on shapes extracted by the active contours technique and the other one directly on the regions of interest found by the algorithm early stages
Summary
Pedestrian detection has been a key topic of the research on intelligent vehicles. This paper describes two modules for pedestrian validation developed for integration into a vision-based obstacle detection system to be installed on an autonomous military vehicle. This system is able to detect all obstacles appearing in the scene and is based on the simultaneous use of two stereo camera systems: two far-infrared cameras and two daylight cameras [3]. The first one has been developed and, in an initial stage, extracts objects shape by means of active contours [4], provides a vote using a neural network-based approach.
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