Stereo algorithm to reduce quantization noise effects in alarm systems

Kevin W. J. Findlay; David Renshaw; Peter B. Denyer

doi:10.1117/12.130883

16 December 1992 Stereo algorithm to reduce quantization noise effects in alarm systems

Kevin W. J. Findlay, David Renshaw, Peter B. Denyer

Proceedings Volume 1766, Neural and Stochastic Methods in Image and Signal Processing; (1992) https://doi.org/10.1117/12.130883
Event: San Diego '92, 1992, San Diego, CA, United States

Abstract

Over the years a considerable amount of research has been conducted in the area of passive stereo vision. Usually attempts have been made to solve the stereo correspondence problem in its most general sense and build an all purpose stereo module. Possible matches are proposed for all parts or edges of the image. The above general approach is not always necessary. Indeed there is evidence that the human vision system only attempts to match a small number of possible edges in a particular scene. In this paper we describe a computationally simple algorithm which takes advantage of the nature of the object being tracked. Disparity measurements are made for the entire edge and statistics used to provide subpixel accuracy. This approach reduces the problems caused by quantization noise when attempts are made to rectify the depth information. We show that stereo algorithms can be used and adapted in an application specific manner to construct viable systems in the areas of alarms and `invisible wall' detection. Results are presented to show the effectiveness of the algorithm in a number of both difficult and simple sequences. In conclusion, we believe our work demonstrates an industrially viable vision system requiring minimal hardware for implementation.

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Kevin W. J. Findlay, David Renshaw, and Peter B. Denyer "Stereo algorithm to reduce quantization noise effects in alarm systems", Proc. SPIE 1766, Neural and Stochastic Methods in Image and Signal Processing, (16 December 1992); https://doi.org/10.1117/12.130883

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KEYWORDS

Cameras

Quantization

Algorithm development

Imaging systems

Detection and tracking algorithms

Filtering (signal processing)

Image processing

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