CUDA技术在视频压缩中的应用

传统的视频压缩技术完全依赖于CPU的工作,占用大量的系统资源。而基于CUDA技术的视频压缩软件不仅减轻系统负担,更大大加快视频处理速度。     

基于Windows平台的立体视频显示研究

本文说明了立体视觉的基本原理,探讨了基于OpenGL立体显示驱动以及基于NVIDIA公司开发的显示驱动的视频立体显示技术的基本方法,给出了立体视频系统的结构,并说明了相关原理。此外,本文还介绍了DirectShow视频处理技术。最后,本文探讨了视觉系统的校准,使系统工作在最佳状态下。     

基于NVIDIA Jetson TX2的道路场景分割

图像语义分割是计算机视觉领域重要研究方向之一,其中基于深度学习的语义分割相较于传统分割算法更为高效可靠,可应用于交通监控、自动驾驶等领域的场景理解阶段.但复杂的分割网络在嵌入式平台上的推理速度较低,难以进行实际应用.因此针对交通监控、无人驾驶等应用背景,在嵌入式平台NVIDIA Jetson TX2上,采用基于深度卷积编解码器结构的图像分割网络,对道路场景进行语义分割,并基于NVIDIA的推理加速器TensorRT2,完成网络模型简化、网络自定义层添加与CUDA并行优化,实现了对网络推理阶段的加速.实验结果表明,加速引擎在TX2上的推理速度约为原模型的10倍,为复杂分割网络在嵌入式平台上的应用提供了支持.     

基于NVIDIA TX2模块的双目视觉信号采集系统设计

针对汽车零配件胶体三维信息检测面临的缺失高帧率、采集实时性的问题,设计了一套基于嵌入式模块的双目视觉信号采集系统。该系统以NVIDIA TX2为核心,在Linux系统下控制4路板级相机采集图像,达到实时控制其中2路相机进行采集、处理、发送的效果。测试表明,该系统体积小,方便移动,处理、传输2路相机中心线速度最高可达140 fps,为PC机后续实时处理提供了保障,具有较好的实用价值。     

基于NVIDIA JetsonTX2的视频行为检测研究

行为检测是计算机视觉领域的一个重要研究方向,在交通监控、人机交互等方面都有着广泛的应用。目前,基于深度学习的C3D行为检测网络与传统行为检测相比,其检测精度虽然有了提高,但存在网络参数量大的问题。为进一步提高检测结果的准确性以及降低网络参数量,采用改进的SqueezeNet与C3D相结合的卷积神经网络,并引入BN层与short-cut结构。将训练模型部署到NVIDIA JetsonTX2上,对视频行为进行分析、检测。实验结果表明,改进后的SqueezeNet-C3D卷积神经网络相比于C3D神经网络在精度上提高了4.4%;改进后的SqueezeNet-C3D网络与SqueezeNet-C3D网络相比,参数量降低了15%。可见该网络具有精度高、参数量少的优点。     

Parallel solution of the subset‐sum problem: an empirical study

The subset‐sum problem is a well‐known NP‐complete combinatorial problem that is solvable in pseudo‐polynomial time, that is, time proportional to the number of input objects multiplied by the sum of their sizes. This product defines the size of the dynamic programming table used to solve the problem. We show how this problem can be parallelized on three contemporary architectures, that is, a 128‐processor Cray Extreme Multithreading (XMT) massively multithreaded machine, a 16‐processor IBM x3755 shared memory machine, and a 240‐core NVIDIA FX 5800 graphics processing unit (GPU). We show that it is straightforward to parallelize this algorithm on the Cray XMT primarily because of the word‐level locking that is available on this architecture. For the other two machines, we present an alternating word algorithm that can implement an efficient solution. Our results show that the GPU performs well for problems whose tables fit within the device memory. Because GPUs typically have memories in the order of 10GB, such architectures are best for small problem sizes that have tables of size approximately 10¹⁰. The IBM x3755 performs very well on medium‐sized problems that fit within its 64‐GB memory but has poor scalability as the number of processors increases and is unable to sustain performance as the problem size increases. This machine tends to saturate for problem sizes of 10¹¹ bits. The Cray XMT shows very good scaling for large problems and demonstrates sustained performance as the problem size increases. However, this machine has poor scaling for small problem sizes; it performs best for problem sizes of 10¹² bits or more. The results in this paper illustrate that the subset‐sum problem can be parallelized well on all three architectures, albeit for different ranges of problem sizes. The performance of these three machines under varying problem sizes show the strengths and weaknesses of the three architectures. Copyright © 2012 John Wiley & Sons, Ltd.     

基于YOLOv3的嵌入式实时视频目标检测算法

深度神经网络在目标检测领域具有优异的检测性能,但其结构复杂、计算量大,难以在嵌入式设备上进行高性能的实时目标检测。针对该问题,提出一种基于YOLOv3的目标检测算法。采用半精度推理策略提高YOLO算法的推理速度,并通过视频运动自适应推理策略充分利用前后帧视频之间目标的关联性,降低深度学习算法的运行频率,进一步提高目标检测速度。在ILSVRC数据集上的实验结果表明,该算法可以在NVIDIA TX2嵌入式平台上实现28 frame/s的视频目标检测,且检测精度与原始的YOLOv3算法相当。