Case Study

使用英特尔® 架构中的深度神经网络检测无人看管行李

随着世界中面临的潜在安全威胁变得越来越多,企业对部署复杂的监控系统的需求开始不断增长。能够作为一个直观的“机器人眼睛”,准确实时地检测无人看管行李的智能系统,已经成为机场、火车站、商场和其他公共区域的安保人员的关键需求。本文介绍了用于火车站的无人看管行李检测的 Microsoft Common Objects in Context (MS-COCO) 检测模式。

1.物体检测算法的演进

图像分类涉及在预定义标签之中预测图像的标签。这会假定图像中有单个相关物体,占据图像的较大部分。检测不仅在于找到物体的类别,而且在于确定物体在图像中的体积。可将物体分层到图像的任何地方,且可以是任何大小(比例)。因此,当图像有多个物体、物体较小,同时需要准确的位置和图像时,物体分类用处不大。

传统的检测方法涉及使用块式方向直方图(SIFT 或 HOG)功能,这无法在标准数据集中实现高精度,如 PASCAL VOC。这些方法会编码物体的低级别特性,因此无法有效区分不同的标签。基于深度学习(卷积网络)的方法成为图像物体检测的领先方法。各种网络拓扑已经随着时间的变化进行了演进,如图 1 所示。

  • Professional
  • Professors
  • Students
  • Linux*
  • Artificial Intelligence
  • Intermediate
  • Caffe*
  • Machine Learning
  • Security
  • From UAV to VR in No Time: Autodesk® ReCap™ Photo Quickly Renders 3D Structures Thanks to Multicore Scalability

    Creating 3D models of an outdoor scene for use in architecture, engineering, and construction is now easier and faster than ever with the help of UAVs, Autodesk ReCap Photo, and high-core-count Intel Xeon processors. Businesses in these sectors can take advantage of the new speed, ease, and availability of 3D modeling based on photogrammetry to meet more deadlines, speed construction time, improve design through better visualizations, and gain competitive advantage.
  • Partners
  • Professional
  • Professors
  • Augmented Reality
  • Computer Vision
  • Graphics
  • Merged Reality
  • Virtual Reality
  • Lab7 Systems Helps Manage an Ocean of Information

    Finding efficient ways to manage the massive amounts of data generated by new technologies is a key concern for many industries. It’s especially challenging in the world of life sciences, where research breakthroughs are based on an ever-expanding ocean of information.

  • Modern Code
  • Intel® Parallel Studio XE
  • Intel® Parallel Studio XE Cluster Edition
  • Intel® Parallel Studio XE Composer Edition
  • Intel® Parallel Studio XE Professional Edition
  • SoftLab-NSK Builds a Universal, Ultra HD Broadcast Solution

    SoftLab-NSK develops complete TV broadcast automation solutions that work with the 4K format and HEVC compression and include functionality for video encoding. When the company wanted to expand its flagship Forward T* line of playout servers, it needed the most efficient solution for video transcoding.

  • Media Processing
  • Power System Infrastructure Monitoring Using Deep Learning on Intel® Architecture

    This paper evaluates the performance of Intel® Xeon® processor powered machines for running deep learning on the GoogleNet* topology (Inception* v3). The functional problem tackled is the identification of power system components such as pylons, conductors, and insulators from the real-world video footage captured by unmanned aerial vehicles (UAVs) or commercially available drones.
  • Professional
  • Professors
  • Students
  • Linux*
  • Artificial Intelligence
  • Python*
  • Intermediate
  • TensorFlow*
  • Unattended Baggage Detection Using Deep Neural Networks in Intel® Architecture

    In a world becoming ever more attuned to potential security threats, the need to deploy sophisticated surveillance systems is increasing. An intellectual system that functions as an intuitive “robotic eye” for accurate, real-time detection of unattended baggage has become a critical need for security personnel at airports, stations, malls, and in other public areas. This article discusses inferencing a Microsoft Common Objects in Context (MS-COCO) detection model for detecting unattended baggage in a train station.

  • Professional
  • Professors
  • Students
  • Linux*
  • Artificial Intelligence
  • Intermediate
  • Caffe*
  • Machine Learning
  • Security
  • Profiling Tensorflow* workloads with Intel® VTune™ Amplifier

    Machine learning applications are very compute intensive by their nature. That is why optimization for performance is quite important for them. One of the most popular libraries, Tensorflow*, already has an embedded timeline feature that helps understand which parts of the computational graph are causing bottlenecks but it lacks some advanced features like an architectural analysis.

  • Linux*
  • Microsoft Windows* 10
  • Artificial Intelligence
  • C/C++
  • Python*
  • Beginner
  • Intermediate
  • Intel® Parallel Studio XE
  • Intel® VTune™ Amplifier
  • VTune
  • TensorFlow
  • timeline
  • JSON
  • Debugging
  • Development Tools
  • Machine Learning
  • Subscribe to Case Study