Stereo-Vision Based System For Object Detection And Recognition

P. Aswin, J. Chandana, Seethal Reghunath, Maya Menon
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引用次数: 3

Abstract

This paper proposes a method for detecting and recognizing the object using Stereo Vision, Scale-Invariant Feature Transform (SIFT) and Fast library for approximate Nearest Neighbors (FLANN) concept with its implementation on an embedded system. Using stereo vision on the microprocessor Raspberry Pi, the implemented system takes the two images produced as input, calculates the disparity map which provides the relative depth information. Using this map and the Scale-Invariant Feature Transform (SIFT), features are obtained and matched with a database having large collection of images. This implementation uses Fast Library for Approximate Nearest Neighbors (FLANN), which unlike the Brute-Force matching algorithm can support large databases. This system gives a voice output when the object is recognized by text to speech conversion.
基于立体视觉的目标检测与识别系统
本文提出了一种利用立体视觉、尺度不变特征变换(SIFT)和快速近似近邻库(FLANN)概念进行目标检测和识别的方法,并在嵌入式系统上实现。实现的系统利用树莓派微处理器上的立体视觉,将生成的两幅图像作为输入,计算视差图,从而提供相对深度信息。利用该映射和比例不变特征变换(SIFT),获得特征并与具有大量图像集的数据库进行匹配。该实现采用了FLANN (Fast Library for Approximate Nearest Neighbors)算法,与蛮力匹配算法不同,FLANN可以支持大型数据库。该系统通过文本到语音的转换来识别对象时,给出语音输出。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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