基于DSP的神经网络实时仿真系统研究

采用DSP设计完成了神经网络实时仿真系统。文章从神经网络协处理器的硬件结构、协处理器中的神经网络协处理器的硬件结构，协处理器中的神经网络算法，神经网络协处理器及其缩主机间数据交换等方面系统地描述了该仿真系统。在神经网络在线算法的实现中，利用DSP能与其宿主机实现并行工作的特点，采用DSP和计算机并行工作；在宿主机中实现BP网络对受控对象的辨识，在神经网络协处理器中完成模糊神经网络在线控制算法，从而加快了神经网络运算速度，使其达到在线控制的要求，文章的最后给出了一个直升机总矩通道在本仿真系统的仿真实例，说明了本系统的实用性。     

高速串行接口在基带系统中的应用研究

现有的LTE-A系统基带部分多采用DSP+FPGA的平台构架。DSP作为主控制器,完成物理层算法及流程控制;FPGA则作为协处理器,负责数据处理和定时。如何快速实现DSP与FPGA间的命令交互以及数据传输成为亟待解决的问题。对类似平台及DSP与FPGA的各种接口进行了研究,提出了采用EMIFA接口负责DSP与FPGA间的命令交互,利用高速串行接口SRIO实现DSP与FPGA间的数据传输的新方案。对该方案进行板级联合调试以及Modelsim SE环境下的仿真。调试与仿真结果表明,该方案具有较高的可行性和通用性。     

基于FPGA和双DSP的实时图像处理器设计

为满足高速实时图像处理的要求,提出一种基于FPGA互联的.以两片TMS320C6416为核心图像数据处理单元的并行系统结构.其中DSP负责图像处理,FPGA负责实现整个系统的数字逻辑及12C总线的配置,实现以FPGA作为主DSP协处理器的方式增加了该系统的灵活性及实时性.结果表明,在基于PCI总线的高速图像数据通道下,该系统可用来进行视频图像的高速采集工作,能够实现快速傅里叶变换、边缘检测等图像处理算法,满足实时图像处理的要求.     

一种嵌入式实时图像跟踪系统设计

该文提出了一套可用于快速视觉信息处理的实时目标跟踪嵌入式视觉系统。系统的硬件设计采用了基于DSP和现场可编程逻辑器件(FPGA)的双处理器结构和基于CMOS的图像采集。软件采用了适于流水线运算的块匹配算法,实现了对目标的快速定位和实时跟踪。     

FPGA在某电视跟踪系统中的应用

介绍了一种以DSP为核心的数字式电视跟踪系统,主要研究了利用FPGA控制、处理数字视频信号的方法,实现了实时的视频叠加。详细讨论了FPGA的控制逻辑及其相关部分结构。采用FPGA实现视频显示信息的叠加,可有效减轻DSP的速度压力,提高系统性能。     

基于FPGA的双机热备外设容错系统仿真设计

针对传统的继电保护冗余系统缺乏对敏感外设容错处理、切换速度慢等问题,提出了基于FPGA的双机热备外设容错系统。该系统由FPGA控制器、DSP控制器、双A/D模块、双继电器模块等外设组成,FPGA控制器完成双机外设模块的故障检测、双机实时切换,为DSP控制器提供A/D实时采样数据、继电器信号接口,DSP控制器对采样数据进行计算与分析,产生继电保护信号,FPGA控制器和DSP控制器通过"心跳"信号互相检测。FPGA控制器时序仿真波形表明:双机外设模块可以实现周期故障自检、双机快速切换。     

嵌入式全景视觉实时目标识别

在嵌入式系统中,使用鱼眼镜头实现全景视觉,完成实时目标跟踪工作.采用FPGA+DSP+PC的硬件系统架构作为识别器,使用SAA7113H芯片采集图像,再由FPGA将采集后的图像存入到SRAM,因为使用鱼眼镜头采集后的图像是畸变的,所以采用FPGA对图像进行矫正,运用Freeman链码进行目标识别,尤其是对直线的识别,这些工作都需要大量的计算,高效的FPGA和Altera公司提供的IP核,加快目标识别速度,以达到目标跟踪的目的.     

基于DSP+FPGA架构的嵌入式运动控制平台设计

针对一体化转台控制所需的运动位置的高速采集与实时显示、多工作模式的运动轨迹和位置的实时控制以及远程控制的通信等要求,设计了一种基于DSP和FPGA的嵌入式运动控制平台.所设计的系统充分利用具有丰富外设资源和浮点运算能力的高性能DSP芯片作为主控制单元,并采用FPGA芯片Nios内核为协处理器实现良好的人机交互控制.实验结果表明,该系统具有高集成度、稳定等特点,已成功应用于一体化转台控制系统.     

基于FPGA的H．264变换量化的高性能的硬件实现

针对H．264视频标准中一个功能频繁调用的变换量化模块，提出了一种高性能的FPGA硬件实现方法。并完成了其硬件原型的设计。该硬件原型包含了从残差形成到宏块重建的变换量化全过程。其可以构成DSP的协处理器，用于完成H．264实时编解码。该硬件原型根据算法特点和数据流特点，采用了流水线控制策略和分时复用技术，同时合理利用FPGA片内资源，从而提高了系统性能。仿真结果表明。该设计能满足高清数字视频的实时处理应用。     

基于FPGA的图像边缘检测器的研究和设计

在构建了一种基于联DSP+FPGA图像处理系统的基础上,论述了一个基于EDA技术的、用FPGA实现图像边缘检测协处理器的设计过程,包括边缘检测算法的选择、系统FPGA的VHDL设计实现和在MAXPLUSII开发环境下的相关仿真结果.该协处理器的像素处理方式采用全硬件并行及流水线技术,经验证,和单独采用单片机或DSP系统相比,其处理速度有显著的提高.     

FPGA–DSP co-processing for feature tracking in smart video sensors

Motion estimation in videos is a computationally intensive process. A popular strategy for dealing with such a high processing load is to accelerate algorithms with dedicated hardware such as graphic processor units (GPU), field programmable gate arrays (FPGA), and digital signal processors (DSP). Previous approaches addressed the problem using accelerators together with a general purpose processor, such as acorn RISC machines (ARM). In this work, we present a co-processing architecture using FPGA and DSP. A portable platform for motion estimation based on sparse feature point detection and tracking is developed for real-time embedded systems and smart video sensors applications. A Harris corner detection IP core is designed with a customized fine grain pipeline on a Virtex-4 FPGA. The detected feature points are then tracked using the Lucas–Kanade algorithm in a DSP that acts as a co-processor for the FPGA. The hybrid system offers a throughput of 160 frames per second (fps) for VGA image resolution. We have also tested the benefits of our proposed solution (FPGA + DSP) in comparison with two other traditional architectures and co-processing strategies: hybrid ARM + DSP and DSP only. The proposed FPGA + DSP system offers a speedup of about 20 times and 3 times over ARM + DSP and DSP only configurations, respectively. A comparison of the Harris feature detection algorithm performance between different embedded processors (DSP, ARM, and FPGA) reveals that the DSP offers the best performance when scaling up from QVGA to VGA resolutions.     

Fast object tracking using adaptive block matching

We propose a fast object tracking algorithm that predicts the object contour using motion vector information. The segmentation step common in region-based tracking methods is avoided, except for the initialization of the object. Tracking is achieved by predicting the object boundary using block motion vectors followed by updating the contour using occlusions/disocclusion detection. An adaptive block-based approach has been used for estimating motion between frames. An efficient modulation scheme is used to control the gap between frames used for motion estimation. The algorithm for detecting disocclusion proceeds in two steps. First, uncovered regions are estimated from the displaced frame difference. These uncovered regions are classified into actual disocclusions and false alarms by observing the motion characteristics of uncovered regions. Occlusion and disocclusion are considered as dual events and this relationship is explained in detail. The algorithm for detecting occlusion is developed by modifying the disocclusion detection algorithm in accordance with the duality principle. The overall tracking algorithm is computationally superior to existing region-based methods for object tracking. The immediate applications of the proposed tracking algorithm are video compression using MPEG-4 and content retrieval based on standards like H.264. Preliminary simulation results demonstrate the performance of the proposed algorithm.     

自适应紧致特征的超像素目标跟踪

目的 针对现有的超像素目标跟踪算法（RST）对同一类中分别属于目标和相似干扰物体的超像素块赋予相同特征置信度,导致难以区分目标和相似干扰物的问题,为此提出自适应紧致特征的超像素目标跟踪算法（ACFST）。方法 该方法在每帧的目标搜索区域内构建适合目标大小的自适应紧致搜索区域,并将该区域内外的特征置信度分别保持不变和降低。处于背景中的相似干扰物体会被该方法划分到紧致搜索区域外,其特征置信度被降低。当依据贝叶斯推理框架求出对应最大后验概率的目标时,紧致搜索区域外的特征置信度低,干扰物体归属目标的程度也低,不会被误判为目标。结果 在具有与目标相似干扰物体的两个视频集进行测试,本文ACFST跟踪算法与RST跟踪算法相比,平均中心误差分别缩减到5.4像素和7.5像素,成功率均提高了11%,精确率分别提高了10.6%和21.6%,使得跟踪结果更精确。结论 本文提出构建自适应紧致搜索区域,并通过设置自适应的参数控制紧致搜索区域变化,减少因干扰物体与目标之间相似而带来的误判。在具有相似物体干扰物的视频集上验证了本文算法的有效性,实验结果表明,本文算法在相似干扰物体靠近或与目标部分重叠时,能够保证算法精确地跟踪到目标,提高算法的跟踪精度,具有较强的鲁棒性,使得算法更能适应背景杂乱、目标遮挡、形变等复杂环境。     

交互式分割视频运动对象的研究与实现

在 MPEG- 4视频编码标准中 ,为了实现基于视频内容的交互功能 ,视频序列的每一帧由视频对象面来表示 ,而生成视频对象面 ,需要对视频序列中运动对象进行有效分割 ,并跟踪运动对象随时间的变化 .在视频分割方法中 ,交互式分割视频对象能满足分割的效率与质量指标要求 ,因此提出了一种交互分割与自动跟踪相结合的方式来分割视频语义对象 ,即在初始分割时 ,依据用户的交互与形态学的分水线分割算法相结合提取视频对象轮廓 ,并用改进的轮廓跟踪方法有效提高视频对象轮廓的精度 ;对后续帧的跟踪 ,采用六参数仿射变换跟踪运动对象轮廓的变化 ,用平移估算的运动矢量作为初始值 ,计算六参数仿射变换的参数 .实验结果表明 ,该方法能有效地分割并跟踪视频运动对象     

Backtracking: Retrospective multi-target tracking

We introduce a multi-target tracking algorithm that operates on prerecorded video as typically found in post-incident surveillance camera investigation. Apart from being robust to visual challenges such as occlusion and variation in camera view, our algorithm is also robust to temporal challenges, in particular unknown variation in frame rate. The complication with variation in frame rate is that it invalidates motion estimation. As such, tracking algorithms based on motion models will show decreased performance. On the other hand, appearance based detection in individual frames suffers from a plethora of false detections. Our tracking algorithm, albeit relying on appearance based detection, deals robustly with the caveats of both approaches. The solution rests on the fact that for prerecorded video we can make fully informed choices; not only based on preceding, but also based on following frames. We start off from an appearance based object detection algorithm able to detect in each frame all target objects. From this we build a graph structure. The detections form the graph’s nodes and the vertices are formed by connecting each detection in a frame to all detections in the following frame. Thus, each path through the graph shows some particular selection of successive detections. Tracking is then reformulated as a heuristic search for optimal paths, where optimal means to find all detections belonging to a single object and excluding any other detection. We show that this approach, without an explicit motion model, is robust to both the visual and temporal challenges.     

Kernel-based object tracking using asymmetric kernels with adaptive scale and orientation selection

Kernel-based object tracking refers to computing the translation of an isotropic object kernel from one video frame to the next. The kernel is commonly chosen as a primitive geometric shape and its translation is computed by maximizing the likelihood between the current and past object observations. In the case when the object does not have an isotropic shape, kernel includes non-object regions which biases the motion estimation and results in loss of the tracked object. In this paper, we propose to use an asymmetric object kernel for improving the tracking performance. An important advantage of an asymmetric kernel over an isotropic kernel is its precise representation of the object shape. This property enhances tracking performance due to discarding the non-object regions. The second contribution of our paper is the introduction of a new adaptive kernel scale and orientation selection method which is currently achieved by greedy algorithms. In our approach, the scale and orientation are introduced as additional dimensions to the spatial image coordinates, in which the mode seeking, hence tracking, is achieved simultaneously in all coordinates. Demonstrated in a set of experiments, the proposed method has better tracking performance with comparable execution time then kernel tracking methods used in practice.     

流水线配置技术在可重构处理器中的应用

提出一种应用于可重构处理器中的流水线配置技术,能够有效减低配置时间,提高应用程序的执行速度。可重构处理器包括通用处理器和一个粗颗粒度的可重构阵列。可重构阵列将处理应用中占据大量执行时间的循环,这些循环将被分解为不同的行在阵列上以流水线的方式执行。该技术在FPGA验证系统上得到了验证。验证的应用包括H.264基准中的整数离散余弦变换和运动估计。相比传统的可重构处理器PipeRench, MorphoSys以及TI的DSP TMS320DM642有大约3.5倍的性能提升。     

基于DSP的指纹识别系统设计

本文提出了一种新型的基于DSP的独立指纹识别系统,构建了高速的数据采集系统。采用FPGA作为协处理器,分担数据计算和扩展接口,根据系统结构改进算法,以此实现一个高效低功耗的嵌入式系统。文中介绍了系统的组成原理、硬件结构设计以及指纹识别算法的处理流程。通过实验,该系统满足实时性要求。