Intelligent transportation video analysis processing system

Abstract

The intelligent traffic video analysis processing system belongs to the field of traffic video analysis, and comprises a video analysis unit, a traffic early warning unit and a video monitoring unit. The video analysis unit of the present application can automatically check the video picture quality of the monitoring camera, judge whether the picture has problems such as black screen, screen flower, insufficient definition, too dark or too bright brightness, high noise, picture jitter and the like, and give a comprehensive quality score and grade; the traffic early warning unit of the present application can identify whether there is a fireworks highlight area or a vehicle congestion and stagnation phenomenon in the picture, generate a warning record and give a processing suggestion when an anomaly is found; the video monitoring unit of the present application centrally displays the online state and analysis results of the camera devices in the jurisdiction, so that the on-duty personnel can understand the overall operation condition on the same page, thereby being able to timely obtain the quality of the video shot by the corresponding camera, and judge whether there is a problem and make timely correction.

Description

Technical Field

[0001] This invention belongs to the field of traffic video analysis, specifically a smart traffic video analysis and processing system. Background Technology

[0002] To ensure the normal operation of highway traffic, video surveillance is used to obtain real-time information on highway traffic and to handle accidents promptly, thereby ensuring the normal operation of road traffic. However, existing traffic video surveillance systems mostly focus on image acquisition and storage, lacking a systematic evaluation mechanism for video quality. Problems such as blurry images, abnormal brightness, and jitter often lead to the loss of key information, affecting the accurate identification of subsequent abnormal events. At the same time, traditional monitoring systems rely heavily on manual inspections to detect traffic anomalies, resulting in slow response times and high missed detection rates, making it difficult to meet the real-time and intelligent requirements of intelligent transportation. Summary of the Invention

[0003] This invention provides an intelligent traffic video analysis and processing system to address the shortcomings of existing technologies.

[0004] This invention is achieved through the following technical solution: The intelligent transportation video analysis and processing system includes a video analysis unit, a traffic early warning unit, and a video monitoring unit. The video analysis unit is used to analyze the acquired traffic road video data, score its quality, and generate a video quality report. The traffic warning unit acquires a video quality report, obtains corresponding video data for the problems found in the video quality report, performs a safety analysis on the data, and then makes a corresponding traffic warning record. The video surveillance unit is used by relevant personnel to obtain video data of the corresponding area to further confirm the warning record after receiving the corresponding traffic warning record.

[0005] In the intelligent transportation video analysis and processing system described above, the video analysis unit reads video frames and calculates quality indicators frame by frame.

[0006] As described above, the quality indicators of the intelligent traffic video analysis and processing system include clarity indicators, brightness indicators, black screen detection indicators, noise level indicators, jitter level indicators, and screen distortion detection indicators.

[0007] In the intelligent transportation video analysis and processing system described above, the clarity index is evaluated by detecting the degree of change at the edges of the video image; The brightness index is evaluated by calculating the average brightness value of the image. The black screen detection index is evaluated by judging whether the screen is almost completely black; The noise level index is assessed by measuring the degree of random noise in the image; The aforementioned jitter level index is assessed by comparing the differences between two consecutive frames; The aforementioned screen distortion detection index is assessed by judging whether there is a large range of color anomalies or signal distortion in the screen.

[0008] As described above, in the intelligent traffic video analysis and processing system, the video analysis unit calculates the quality indicators and gives a quality score from 0 to 100, and divides the score into four levels: 85 points and above is excellent, 70 to 84 points is good, 50 to 69 points is average, and below 50 points is poor.

[0009] As described above, in the intelligent transportation video analysis and processing system, if the video analysis unit provides a quality score, a black screen or more than 20% of the video screen is distorted, it is directly judged as 0 points. If none of the above situations occur, each of the following indicators is assigned a base score of 20 points: clarity, brightness, noise level, jitter, and distortion detection. The corresponding coefficients are obtained through quality indicator analysis, multiplied by the corresponding base scores, and then added together to obtain the final quality score.

[0010] As described above, the safety analysis of the traffic early warning unit in the intelligent traffic video analysis and processing system includes smoke and fire detection and congestion detection.

[0011] As described above, the intelligent traffic video analysis and processing system analyzes the bright red and yellow areas in the image for smoke and fire detection. When the area of ​​the bright pixels reaches a specified size, it is determined that there may be abnormal smoke and fire at that location, and a corresponding traffic warning record is generated. The congestion detection compares the current image with the previously scanned image. If the image changes little and the overall brightness is low, it is determined that there may be vehicle congestion or stagnation at that location, and a corresponding traffic warning record is generated.

[0012] As described above, the traffic early warning record in the intelligent traffic video analysis and processing system includes the early warning time, location name, early warning type, early warning level, early warning description, and handling suggestions.

[0013] In the intelligent traffic video analysis and processing system described above, the warning time is the moment when an anomaly is detected; The location names refer to the names and locations of the cameras that generated the warnings; The warning types mentioned are abnormal fireworks or congestion / delay; The warning levels include Level 1 and Level 2. Level 1 warnings require immediate attention, while Level 2 warnings suggest a review. The warning description uses text to describe the detected anomaly; The proposed handling recommendations provide solutions based on this type of anomaly.

[0014] The advantages of this invention are: the video analysis unit can automatically check the video quality of surveillance cameras, determine whether there are problems such as black screen, distorted screen, insufficient clarity, excessively dark or bright brightness, high noise, or image jitter, and provide a comprehensive quality score and rating; the traffic warning unit can identify whether there are bright areas of smoke or fire or traffic congestion in the image, generate warning records and provide handling suggestions when an anomaly is detected; the video monitoring unit can centrally display the online status and analysis results of camera devices within the jurisdiction, allowing on-duty personnel to understand the overall operation on a single page, thereby enabling them to promptly obtain the quality of the video captured by the corresponding cameras, determine whether there are problems and make timely corrections, and automatically identify safety hazards and generate warning records, thereby reducing the difficulty of manual monitoring, ensuring efficient processing and analysis of traffic videos and rapid detection and handling of corresponding accidents, and ensuring smooth traffic operation. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the quality scoring of the present invention; Figure 2 This is a schematic diagram of the traffic warning record of the present invention; Figure 3 This is a schematic diagram of the monitoring panel of the video monitoring unit of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] The intelligent transportation video analysis and processing system includes a video analysis unit, a traffic early warning unit, and a video monitoring unit. The video analysis unit is used to analyze the acquired traffic road video data, score its quality, and generate a video quality report. The traffic warning unit acquires a video quality report, obtains corresponding video data for the problems found in the video quality report, performs a safety analysis on the data, and then makes a corresponding traffic warning record. The video surveillance unit is used by relevant personnel to obtain video data of the corresponding area to further confirm the warning record after receiving the corresponding traffic warning record.

[0019] Preferably, the video analysis unit described in this embodiment reads video frames (using the built-in simulated frame in demonstration mode, and reading actual video frames when accessing a real video source) and calculates quality indicators frame by frame.

[0020] Preferably, the quality indicators described in this embodiment include clarity indicators, brightness indicators, black screen detection indicators, noise level indicators, jitter level indicators, and screen distortion detection indicators.

[0021] Preferably, the sharpness index described in this embodiment is evaluated by detecting the degree of change at the edges of the video image; the higher the value, the sharper the image. The brightness index is evaluated by calculating the average brightness value of the screen. Moderate is best; too dark may pose a risk of black screen, while too bright may result in loss of screen details. The black screen detection index is evaluated by judging whether the screen is almost completely black; The noise level index is assessed by measuring the degree of random noise in the image. Excessive noise will affect the image's recognizability. The aforementioned jitter level index is assessed by comparing the differences between two consecutive frames; the greater the difference, the more severe the jitter. The aforementioned screen distortion detection index is assessed by judging whether there is a large range of color anomalies or signal distortion in the screen.

[0022] like Figure 1 As shown, preferably, the video analysis unit described in this embodiment calculates the quality indicators and gives a quality score from 0 to 100, and divides the score into four levels: 85 points and above is excellent, 70 to 84 points is good, 50 to 69 points is average, and below 50 points is poor.

[0023] Preferably, in this embodiment, if the video analysis unit provides a quality score, a black screen or more than 20% of the video screen is distorted, the score is directly determined to be 0. If none of the above situations occur, each of the following indicators is assigned a base score of 20 points: clarity, brightness, noise level, jitter, and distortion detection. The corresponding coefficients are obtained through quality indicator analysis, multiplied by the corresponding base scores, and then added together to obtain the final quality score.

[0024] Preferably, the safety analysis of the traffic early warning unit described in this embodiment includes smoke detection and congestion detection.

[0025] Preferably, the smoke detection described in this embodiment analyzes the bright red and yellow areas in the image. When the area of ​​the bright pixels reaches a specified size, it is determined that there may be an abnormality in the smoke at that location, and a corresponding traffic warning record is generated. The congestion detection compares the current image with the previously scanned image. If the image changes little and the overall brightness is low, it is determined that there may be vehicle congestion or stagnation at that location, and a corresponding traffic warning record is generated.

[0026] like Figure 2 As shown, preferably, the traffic warning record in this embodiment includes the warning time, location name, warning type, warning level, warning description, and handling suggestions.

[0027] Preferably, the warning time described in this embodiment is the moment when an anomaly is detected; The location names refer to the names and locations of the cameras that generated the warnings; The warning types mentioned are abnormal fireworks or congestion / delay; The warning levels include Level 1 and Level 2. Level 1 warnings require immediate attention, while Level 2 warnings suggest a review. The warning description uses text to describe the detected anomaly; The proposed handling suggestions are based on the specific measures to be taken for this type of anomaly, such as "notifying the on-duty personnel to check the location and coordinating on-site handling if necessary" or "checking the real-time footage of the location to confirm whether traffic control or dispatching is required."

[0028] Preferably, in terms of hardware, the computer running the software needs an Intel Core i5 or higher processor, 8GB or more of memory, and 500GB or more of hard disk space. In terms of software, the computer needs to have a 64-bit operating system of Windows 10 or Windows 11 installed, and Python 3.9 or a higher version installed. The components that the software depends on include Flask, OpenCV, and NumPy. These components are configured through a one-click installation command before first use. Users can access all functions by accessing the system page through the Chrome or Edge browser.

[0029] like Figure 3 As shown, preferably, the monitoring panel of the video monitoring unit in this embodiment is divided into four areas from top to bottom; The top section contains the title bar and operation button area. The left side displays the software name and version number, as well as the current service running status; the right side has two buttons—"Generate Quality Report" and "Generate Warning Record"—which are the two most frequently used operation entry points in daily use. Below the title bar is the overview area, which displays the current operational status in the form of four cards. The first card displays the "Overall Score" and the current quality level (Excellent / Good / Medium / Poor). If no quality report has been generated, this area is displayed as unanalyzed. The second card displays the number of "Online Devices," indicating the number of cameras currently online. The third card displays the number of "Current Warnings," indicating the number of traffic anomaly warnings recently scanned and recorded. The fourth card displays the "Number of Detected Frames," indicating how many video frames were processed in the most recent quality analysis.

[0030] Below the overview area is the operation result prompt bar, which displays the title and detailed description of the most recent operation in a prominent horizontal bar. For example, after clicking to generate a quality report, it will display "Quality report has been generated" as well as the comprehensive conclusion and handling suggestions for this test.

[0031] Below that is the main content area, with a video quality analysis panel on the left and a device status list on the right. The quality analysis panel displays detailed values ​​for six indicators (clarity, brightness, noise, and jitter), as well as two abnormal markers: black screen and distorted screen. Each indicator has a progress bar next to it that visually displays the value, along with the specific numbers. The bottom of the panel also displays the results, conclusions, and processing suggestions for this quality report, such as "This test read 12 frames, with a comprehensive score of 87.5 points, and the quality level is good" and "The current image quality can be used for subsequent traffic monitoring and early warning analysis."

[0032] The device status list displays information for each camera in card format: device name, installation location, and current status (online / offline). The status is distinguished by different colors - online devices are marked in green, and offline devices are marked in orange. At the bottom of the page is a warning record table, listing recently generated warning information, including six columns: time, location name, warning type, warning level, warning description, and handling suggestions. If there are no warning records yet, the table will display "No warning records available".

[0033] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. Intelligent transportation video analysis processing system, characterized in that: It includes video analysis units, traffic early warning units, and video surveillance units; The video analysis unit is used to analyze the acquired traffic road video data, score its quality, and generate a video quality report. The traffic warning unit acquires a video quality report, obtains corresponding video data for the problems found in the video quality report, performs a safety analysis on the data, and then makes a corresponding traffic warning record. The video surveillance unit is used by relevant personnel to obtain video data of the corresponding area to further confirm the warning record after receiving the corresponding traffic warning record.

2. The intelligent transportation video analysis and processing system according to claim 1, characterized in that: The video analysis unit reads video frames and calculates quality indicators frame by frame.

3. The intelligent transportation video analysis and processing system according to claim 2, characterized in that: The quality indicators include clarity, brightness, black screen detection, noise level, jitter, and screen distortion detection.

4. The intelligent transportation video analysis and processing system according to claim 1, characterized in that: The aforementioned sharpness index is assessed by detecting the degree of change at the edges of the video frame; The brightness index is evaluated by calculating the average brightness value of the image. The black screen detection index is evaluated by judging whether the screen is almost completely black; The noise level index is assessed by measuring the degree of random noise in the image; The aforementioned jitter level index is assessed by comparing the differences between two consecutive frames; The aforementioned screen distortion detection index is assessed by judging whether there is a large range of color anomalies or signal distortion in the screen.

5. The intelligent transportation video analysis and processing system according to claim 3, characterized in that: The video analysis unit calculates the quality indicators and gives a quality score from 0 to 100. The score is divided into four levels: 85 points and above is excellent, 70 to 84 points is good, 50 to 69 points is average, and below 50 points is poor.

6. The intelligent transportation video analysis and processing system according to claim 5, characterized in that: When the video analysis unit gives a quality score, if a black screen or more than 20% of the video screen is distorted, it will be directly judged as 0 points. If none of the above situations occur, each of the following indicators will be assigned a base score of 20 points: clarity, brightness, noise level, jitter, and distortion detection. The corresponding coefficients will be obtained through quality indicator analysis, multiplied by the corresponding base scores, and then added together to obtain the final quality score.

7. The intelligent transportation video analysis and processing system according to claim 1, characterized in that: The safety analysis of the traffic early warning unit includes smoke and fire detection and congestion detection.

8. The intelligent transportation video analysis and processing system according to claim 7, characterized in that: The smoke and fire detection analyzes the bright red and yellow areas in the image. When the area of ​​the bright pixels reaches a specified size, it is determined that there may be an abnormality of smoke and fire at that location, and a corresponding traffic warning record is generated. The congestion detection compares the current image with the previously scanned image. If the image changes little and the overall brightness is low, it is determined that there may be vehicle congestion or stagnation at that location, and a corresponding traffic warning record is generated.

9. The intelligent transportation video analysis and processing system according to claim 1, characterized in that: The traffic warning record includes the warning time, location name, warning type, warning level, warning description, and handling suggestions.

10. The intelligent transportation video analysis and processing system according to claim 9, characterized in that: The warning time is the moment when the anomaly is detected; The location names refer to the names and locations of the cameras that generated the warnings; The warning types mentioned are abnormal fireworks or congestion / delay; The warning levels include Level 1 and Level 2. Level 1 warnings require immediate attention, while Level 2 warnings suggest a review. The warning description uses text to describe the detected anomaly; The proposed handling recommendations provide solutions based on this type of anomaly.

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