智能视频监控算法及硬件实现研究
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摘要
随着经济技术的发展和人们安全意识的提高,作为在安全方面应用最早且最广泛使用的视频监控显得愈发重要。它利用摄像机使相关人员能够远程实时监控一些重要的区域。目前视频监控正向智能视频监控方向发展。智能视频监控具有人所没有的优点,逐渐将人从视频监控中解放出来。它主要基于对视频内容的分析,检测出目标,对目标跟踪和识别,并分析其行为特征。智能视频监控离不开准确而可靠的视频分析算法,这些算法往往需要处理大量数据,消耗相当多的计算资源,给监控的实时性带来很大的挑战。目前出现了很多基于FPGA或芯片的硬件模块或平台作为视频分析引擎用于智能视频监控,加快了视频处理速度使监控更加流畅。本文从智能视频监控算法和视频分析引擎中硬件实现这两部分开展研究,主要完成以下几个方面的工作。
(1)研究了运动检测中的背景建模方法,提出一种基于拉普拉斯分解和混合高斯模型的背景建模算法。利用金字塔变换把图像分解为图像的高频和低频部分,只对低频部分信息中像素的静态特征和动态特征用混合高斯模型建模,最后用高频信息还原得到原始尺度下的运动物体。该方法降低了易受扰动的高频部分对模型的干扰,也减少了存储低频信息所需的存储空间,在测试时取得良好的效果。同时还提出了改进的相邻帧差法并用硬件实现了该算法功能,极大的提高了检测速度和效率。
(2)分析Adaboost算法特点及其改进形式,研究了其在人脸检测和目标跟踪中的应用,并通过实验比较分析了几种基于boosting的目标跟踪算法。
(3)人脸实时检测需要庞大的计算量和软硬件的支持,研究了基于Adaboost算法的人脸检测硬件实现架构,提出一种并行计算Haar矩形特征实现人脸检测的架构。通过将分类器特征分组并定义相应的处理单元,引入多组架构进一步提升并行计算能力,为了减少因流水线深度增加带来的负面影响,在分类器中不同阶段采用不同的预测方式。同时还提出了基于ASIC的人脸检测硬件架构。
(4)在视频监控所用的芯片中,集成视频分析引擎的芯片往往规模很大,会生成大量的测试向量。研究了测试向量压缩技术,提出了两种测试向量压缩算法,都是根据测试向量间存在的大量相容关系,将相容关系最大的合并为一组,一种是用FDR-BC对合并后的向量编码,另一种是用CBCT方法对合并后的向量三种游程进行编码。将这两种方法分别与典型的压缩算法作比较,压缩效果显著。同时对这两种压缩方法分别设计了相应的解码器。
Video surveillance is becoming more important as the first and most widely used method along with the development of economy and safety consciousness. It enables people to monitor some important areas remotely in real time. The method currently is on the trend of intelligent video surveillance, which has some advantages that humans have not and gradually released people from video surveillance. The intelligent video surveillance is based on video content analysis, which includes detecting objects, tracking and identifying them, and analyzing their behaviors. Accurate and reliable video content analysis algorithms are indispensable for the surveillance system. However these algorithms generally have to process huge data and consume more computing resources, which bring great challenge to real-time monitoring. Recently there appear some hardware modules or platforms based on FPGA or chip as the video analysis engine for intelligent video surveillance. The video analysis engine improves processing speed and makes the surveillance fluent. We pay more attention to both the algorithm of intelligent video surveillance and the hardware implementation of video analysis engine in the thesis. The main research work consists of four parts as follows.
(1) We have studied the background model in motion detection, and proposed a background model algorithm using mixture of Gaussians and Laplacian pyramid decomposition. The Laplacian pyramid is employed to decompose the image into low-frequency and high-frequency parts. We only make use of the static and dynamic features of pixels in low-frequency parts to model the background using mixture of Gaussians. After the motion objects in low-frequency parts have been extracted, we begin to use high-frequency parts to restore the objects in original size. The method not only reduces the disturbance of high-frequency parts to the model, but also decreases the demand for the memory space. The method achieve good results based on the experiments we have done using test videos. We have also presented the method of improved temporal differencing and its hardware implementation, which significantly increase the detection speed and efficiency.
(2) The features and improved forms of Adaboost algorithm have been analyzed in the thesis. We also made some research on its application in face detection and object tracking, and compared some tracking algorithms based on boosting.
(3) Face detection cost huge computing resources and need the support of software and hardware. We have studied some hardware architectures of face detection based on Adaboost, and proposed a novel architecture parallelly computing Haar features. By dividing feature set into groups and defining corresponding processing element, we introduce a multi-group architecture to further improve its parallelly computing power. We adopt different prediction mechanisms for different stages in the classifiers in order to reduce the side effect of the increased pipeline depth. Hardware architecture for face detection based on ASIC has been presented in the end.
(4) The chips used in video surveillance which include video content analysis are usually large in scale, which will generate a great number of test vectors. We have proposed two kinds of test vector compression methods, both of which are based on the compatibility among test vectors. Test vectors are grouped based on their compatibility relationship and the test vectors in the group are merged into one vector. One compression technology adopts FDR-BC to encode the merged vectors, and the other employs CBCT to encode three kinds of runs in the merged vectors. Experiments with the test sets show that the method can achieve higher compression ratio compared with some classic compression methods. We also designed two corresponding decoders.
(1)研究了运动检测中的背景建模方法,提出一种基于拉普拉斯分解和混合高斯模型的背景建模算法。利用金字塔变换把图像分解为图像的高频和低频部分,只对低频部分信息中像素的静态特征和动态特征用混合高斯模型建模,最后用高频信息还原得到原始尺度下的运动物体。该方法降低了易受扰动的高频部分对模型的干扰,也减少了存储低频信息所需的存储空间,在测试时取得良好的效果。同时还提出了改进的相邻帧差法并用硬件实现了该算法功能,极大的提高了检测速度和效率。
(2)分析Adaboost算法特点及其改进形式,研究了其在人脸检测和目标跟踪中的应用,并通过实验比较分析了几种基于boosting的目标跟踪算法。
(3)人脸实时检测需要庞大的计算量和软硬件的支持,研究了基于Adaboost算法的人脸检测硬件实现架构,提出一种并行计算Haar矩形特征实现人脸检测的架构。通过将分类器特征分组并定义相应的处理单元,引入多组架构进一步提升并行计算能力,为了减少因流水线深度增加带来的负面影响,在分类器中不同阶段采用不同的预测方式。同时还提出了基于ASIC的人脸检测硬件架构。
(4)在视频监控所用的芯片中,集成视频分析引擎的芯片往往规模很大,会生成大量的测试向量。研究了测试向量压缩技术,提出了两种测试向量压缩算法,都是根据测试向量间存在的大量相容关系,将相容关系最大的合并为一组,一种是用FDR-BC对合并后的向量编码,另一种是用CBCT方法对合并后的向量三种游程进行编码。将这两种方法分别与典型的压缩算法作比较,压缩效果显著。同时对这两种压缩方法分别设计了相应的解码器。
Video surveillance is becoming more important as the first and most widely used method along with the development of economy and safety consciousness. It enables people to monitor some important areas remotely in real time. The method currently is on the trend of intelligent video surveillance, which has some advantages that humans have not and gradually released people from video surveillance. The intelligent video surveillance is based on video content analysis, which includes detecting objects, tracking and identifying them, and analyzing their behaviors. Accurate and reliable video content analysis algorithms are indispensable for the surveillance system. However these algorithms generally have to process huge data and consume more computing resources, which bring great challenge to real-time monitoring. Recently there appear some hardware modules or platforms based on FPGA or chip as the video analysis engine for intelligent video surveillance. The video analysis engine improves processing speed and makes the surveillance fluent. We pay more attention to both the algorithm of intelligent video surveillance and the hardware implementation of video analysis engine in the thesis. The main research work consists of four parts as follows.
(1) We have studied the background model in motion detection, and proposed a background model algorithm using mixture of Gaussians and Laplacian pyramid decomposition. The Laplacian pyramid is employed to decompose the image into low-frequency and high-frequency parts. We only make use of the static and dynamic features of pixels in low-frequency parts to model the background using mixture of Gaussians. After the motion objects in low-frequency parts have been extracted, we begin to use high-frequency parts to restore the objects in original size. The method not only reduces the disturbance of high-frequency parts to the model, but also decreases the demand for the memory space. The method achieve good results based on the experiments we have done using test videos. We have also presented the method of improved temporal differencing and its hardware implementation, which significantly increase the detection speed and efficiency.
(2) The features and improved forms of Adaboost algorithm have been analyzed in the thesis. We also made some research on its application in face detection and object tracking, and compared some tracking algorithms based on boosting.
(3) Face detection cost huge computing resources and need the support of software and hardware. We have studied some hardware architectures of face detection based on Adaboost, and proposed a novel architecture parallelly computing Haar features. By dividing feature set into groups and defining corresponding processing element, we introduce a multi-group architecture to further improve its parallelly computing power. We adopt different prediction mechanisms for different stages in the classifiers in order to reduce the side effect of the increased pipeline depth. Hardware architecture for face detection based on ASIC has been presented in the end.
(4) The chips used in video surveillance which include video content analysis are usually large in scale, which will generate a great number of test vectors. We have proposed two kinds of test vector compression methods, both of which are based on the compatibility among test vectors. Test vectors are grouped based on their compatibility relationship and the test vectors in the group are merged into one vector. One compression technology adopts FDR-BC to encode the merged vectors, and the other employs CBCT to encode three kinds of runs in the merged vectors. Experiments with the test sets show that the method can achieve higher compression ratio compared with some classic compression methods. We also designed two corresponding decoders.
引文
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