People counting is a challenging task with many applications. We propose a method with a fixed stereo camera that is based on projecting a template onto the depth image. The method was tested on a challenging outdoor dataset with good results and runs in real time.
In this paper we propose a head detection method using range data from a stereo camera. The method is based on a technique that has been introduced in the domain of voxel data. For application in stereo cameras, the technique is extended (1) to be applicable to stereo data, and (2) to be robust with regard to noise and variation in environmental settings. The method consists of foreground selection, head detection, and blob separation, and, to improve results in case of misdetections, incorporates a means for people tracking. It is tested in experiments with actual stereo data, gathered from three distinct real-life scenarios. Experimental results show that the proposed method performs well in terms of both precision and recall. In addition, the method was shown to perform well in highly crowded situations. From our results, we may conclude that the proposed method provides a strong basis for head detection in applications that utilise stereo cameras.
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Met toestemming overgenomen uit Microniek, 2020, nr. 5 A stereo-vision system that was developed for application in mobile robots turned outto lack depth resolution in the background of the pictures. A simulator was built togain understanding of the parameters that influence depth estimation in stereo vision.In this article we will explain how these properties influence depth resolution andprovide a link to the webtool that was made to interactively observe and evaluate theresulting depth resolution when the parameters are varied. This tool makes it possibleto find the correct hardware that provides the resolution required, or to determinethe resolution for specific hardware.
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