Cloud expansion, part 3rd: Exploring video analysis in the cloud

Perform video and image analysis, monitoring, and security with a variety of methods, tools, and system designs

Explore and analyze methods, tools, and systems designed to perform video and image analysis through cloud expansion. Earlier in this series, video analysis requires a more balanced "data-centric computing architecture" than traditional computing-centric, scalable, high-performance computing. The author describes how to use OpenCV and similar tools for digital video analysis and methods to extend such analysis using cluster and distributed system design.

In previous installments, a coprocessor designed specifically for video analysis and new OPENVX hardware acceleration was discussed, which can be applied to the computer Vision (CV) sample provided in this article. This new data-centric CV and video analysis technology requires system designers to rethink application software and system design to meet stringent requirements, such as real-time monitoring and security for large, public facilities and infrastructure, and a more entertaining, interactive, and secure world.

Public Safety and security

Integrated video analysis in public places may be the best way to ensure public safety, providing the law enforcement sector with digital expertise and increasing the potential for threat detection and prevention of public safety incidents. At the same time, this demand must be weighed against privacy rights, which would become a contentious issue if they were abused or not clearly understood. For example, facial detection extensions for facial recognition (shown in Figure 1) have distinct recognition features that can be used to track the movement of individuals in public places. For many people, facial analysis may be seen as a privacy violation, and the use of CV and video analysis should be subject to monitoring and privacy rights laws and policies, and undeniably, product or service developers may want to first consider the best practices proposed by the U.S. Federal Business Council.

The use of standards (such as those from the Motion picture experts Group, or MPEG) to encode video for compressing, transmitting, decompressing, and displaying digital video has led to dramatic changes in the computing industry, From social networking media and amateur digital theaters to improved training and educational change. The tools for decoding and using digital video are widely used every day, but video analysis requires tools for encoding and analyzing uncompressed video frames, such as Open Computer Vision (OpenCV). An easily accessible and powerful digital Video codec tool is FFmpeg; for static images, GNU image processing (GIMP) is useful (see Resources for related links). With these 3 basic tools, open source developers are fully able to start exploring computer vision (CV) and video analysis. However, before you analyze these tools and development methods, let's start by providing a better definition of these words and think about what they are used for.

In the first article in this series, cloud extensions, part 1th: Building your own cloud and using on-demand HPC extensions provides a simple example of using OpenCV, which implements Canny edge conversions on continuous real-time video from a Linux Web camera. This is an example of a CV application that you can use as the first step in segmenting an image. In general, CV applications involve the acquisition of digital image formats, images and image sequences (films), processing and conversion, segmentation, recognition, and final scene descriptions, which represent the image elements of the illuminance point. The best way to understand the purpose of the CV is to view the sample. Figure 1 shows an analysis of facial features detected in a face and using OpenCV. Note that in this simple example, the Haar Cascade method (a machine learning algorithm) is used to perform instrumentation analysis. The algorithm can most accurately detect the face and eyes that have not been blocked (for example, when my youngest son's face is turned to one side) or that has been obscured by shadows, and the face and eyes when the subject is not squinting. The following may be the most important comment on the CV: it is not a simple question. Researchers in this field have often noticed that although it has made great strides since its birth more than 50 years ago, most applications still fail to catch up with a 2-year-old's ability to distinguish and recognize the scene, especially when it comes to the ability to generalize and perform recognition under a wide range of conditions (illumination, size change, direction and environment).

Figure 1. Using OpenCV to perform facial recognition

To help you understand the profiling methods used in the CV, I created a smaller set of tests for the Alaska State Anchorage area image, which can be downloaded by downloading. These images have been processed using GIMP and OpenCV. I developed the C + + code to use the OpenCV application programming interface for Linux WEB cameras, pre-captured images, or MPEG videos. Using CV to understand video content (image sequences, whether in real time or from a pre-collected image sequence database) is often called video analysis.

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