Terrace monitoring video compression method and system based on deep learning

A technology for monitoring video and compression methods, applied in the field of video processing technology and deep learning, to solve problems such as inability to adapt to demand and increasingly diverse video use cases

Active Publication Date: 2019-06-28
CIVIL AVIATION UNIV OF CHINA
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  • Claims
  • Application Information

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Problems solved by technology

While they are well-designed and thoroughly tuned, they are hard-coded and thus u

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  • Terrace monitoring video compression method and system based on deep learning
  • Terrace monitoring video compression method and system based on deep learning
  • Terrace monitoring video compression method and system based on deep learning

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Embodiment Construction

[0034] In order to further understand the invention content, characteristics and effects of the present invention, the following examples are given, and detailed descriptions are as follows in conjunction with the accompanying drawings:

[0035] A video is composed of many frames of pictures, and the pictures are divided into vector graphics and bitmaps. A vector image, also known as an object-oriented image, is mathematically defined as a series of points connected by lines. Graphical elements in vector files are called objects. Each object is a self-contained entity with attributes such as color, shape, outline, size, and screen position. Since each object is a self-contained entity, it can be moved and changed multiple times while maintaining its original clarity and curvature without affecting other objects in the legend. Vector-based drawings are resolution-independent, which means they can be displayed on the output device at the highest resolution possible. According...

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Abstract

The invention relates to a terrace monitoring video compression method and system based on deep learning. The terrace monitoring video compression method comprises the steps: 1, generating a background picture; 2, determining the overall brightness, color and light parameters of each frame of image; 3, recording pictures, lighting time ranges and building positions of lighting and turning off states of the terminal building and the boarding bridge; 4, detecting an airplane, a vehicle and a person in each frame of image in the original video by using a region-based convolutional neural network,and cutting off the detected objects according to the positioning positions of an algorithm; 5, sequentially storing the contents processed in the steps 1 to 4 into a linked list in a node form according to a processing sequence; 6, decompressing the video; 7, covering the building picture in the rendered background picture; and 8, taking out the plurality of object videos from the linked list, covering each frame of picture in the video into a corresponding background picture according to the position information, and playing the pictures at a speed of 25 frames per second.

Description

technical field [0001] The invention belongs to the field of video processing technology and deep learning, and in particular relates to a method and system for compressing apron monitoring video based on deep learning. Background technique [0002] With the continuous expansion of the scope of video usage and the increasing demand for high-quality video, the video supplier expands the video parameter space by using higher spatial resolution, frame rate and dynamic range, which greatly increases the storage required for video. bit rate. Especially in schools, banks, civil aviation and other fields, the state has stricter requirements on the storage time of surveillance video. In the field of civil aviation, Chapter 15 of "Civil Transport Airport Safety and Security Facilities" (MH / T7003-2017) clarifies the specifications for airport video surveillance, which requires video surveillance to be stored for no less than 90 days to meet the requirements of the "Anti-Terrorism Law...

Claims

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Application Information

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IPC IPC(8): H04N19/426H04N19/48H04N5/14H04N5/262H04N5/272H04N5/265
Inventor 吕宗磊徐先红
Owner CIVIL AVIATION UNIV OF CHINA
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