The development trend of modern network video surveillance systems is large-scale networking, distributed deployment, and intelligent monitoring. Intelligent video analysis based on computer vision incorporates techniques in multiple disciplines such as image processing, pattern recognition, artificial intelligence, automatic control, and computer science. Compared with the traditional video surveillance system, the intelligent video surveillance system can extract valuable information from the original video, change the artificial servo to take the initiative to identify, change the post-event analysis as the incident analysis and alarm.
Intelligent Video Surveillance Technology (IVS: Intelligent Video Surveillance) analyzes the video content of surveillance scenes based on computer vision technology, extracts key information in the scene, generates high-level semantic understanding, and forms monitoring methods for corresponding events and alarms. If the camera is seen as the human eye, the intelligent video surveillance system can be understood as the human brain. Intelligent video surveillance technology often utilizes the powerful computing capabilities of the processor chip to perform high-speed analysis on massive data in video frames, filter out information that users do not care about, and provide only useful information for the monitor.
The Generation and Characteristics of Video Intelligence Analysis
There are two basic problems that smart video systems need to solve: one is to free security personnel from the complex and tedious “eye-screen†task, and the machine completes this part of the work; the other is to quickly perform in massive amounts of video data. Search for the image you are looking for. According to statistical analysis, security operators will miss 90% of video information after staring at the screen video wall for more than 10 minutes, which will make this work meaningless. In the London Underground bombings, security personnel took 70 man hours to find the needed information on a large number of tapes. Therefore, based on the above two points, choosing a video analysis system will be able to free people from the arduous work and increase efficiency. The intelligent video analysis system will become the core component of the video surveillance system in the future.
The background of intelligent video analysis comes from the most basic requirements. For example, when security duty personnel face hundreds of cameras, they cannot really prevent or interfere with the risks. Most of them rely on playback-related video post-processing; Some non-security applications, such as business flow statistics, target recognition (license plates, faces, etc.), also require automatic intelligent statistical identification. Intelligent video analysis transfers the analysis and recognition of events to a computer or a chip, freeing the on-duty personnel from the work of the “staring†monitor. When the computer finds a problem, an alarm is generated and the on-duty personnel performs the corresponding processing operations.
The main advantages of intelligent video surveillance:
• Fast response time: Millisecond alarm trigger response time;
• More effective surveillance: Security personnel only need to pay attention to relevant information;
• Powerful data retrieval and analysis capabilities: Provides rapid response time and survey time.
Motion detection is the foundation
The vast majority of intelligent video analysis is based on the moving target detection technology, that is, the first intelligent analysis system can accurately complete the detection of the moving target, effectively separate the moving object from the background of the image, and extract the moving target information.
From the practical application of computer vision, the main challenges and problems that need to be solved in moving target detection, recognition, and analysis can be summed up in three aspects, namely, the robustness, accuracy, and real-time performance of the algorithm.
Robustness
Robustness is the robustness of the system to characterize the insensitivity of the control system to perturbations in characteristics or parameters. The robustness of the moving object detection algorithm is able to achieve continuous, stable detection, analysis and recognition of moving objects under various environmental conditions.
The most important reasons that affect the robustness of the algorithm are the following: changes in the state of the target, changes in the ambient light, partial irregularities caused by the target occlusion, and temporary disappearance of the target caused by all occlusion.
accuracy
The detection and recognition of moving targets are different for different applications, and their detection and identification rates are different. It is almost impossible to achieve 100% detection success, that is, there are false detections and missed detections. Because the actual monitoring scene environment is complex and ever-changing, there are a lot of noise and interference, optimization through the algorithm can improve the detection accuracy, and often can only be based on actual needs, in the false detection rate (false alarm rate) and missed detection. The balance between rates (missing rates) seeks balance.
real-time
A practical intelligent video surveillance system must have the ability to process video image sequences in real time. Because the processing method of video dynamic image is based on the processing of two-dimensional digital signals, the object to be processed contains a huge amount of data and information, requiring that the algorithm cannot be calculated too complicated and must be fast and real-time. For real-time analysis and warning tasks, the computational complexity is critical so that more resources can be allocated to more advanced tasks. However, real-time and robustness are often contradictory. How to seek balanced development is the key to technology.
In particular, it has been pointed out that currently the company has independently developed a video motion detection algorithm, which is less affected by the light and lens jitter compared to the classical motion detection algorithm, and has less computation time, which is more suitable for real-time product development. At the same time, the package development kit (SDK) is based on the autonomous algorithm and integrated in the company's digital security system software platform and network camera series products to achieve system-level and product-level reliable applications. At the same time, the project team is building video libraries for various scenarios extensively, testing the video for multiple scenarios using its own algorithms to better refine the algorithms and reduce the number of parameters that need to be adjusted to better meet the actual application requirements. .
Intelligent Video Surveillance Technology (IVS: Intelligent Video Surveillance) analyzes the video content of surveillance scenes based on computer vision technology, extracts key information in the scene, generates high-level semantic understanding, and forms monitoring methods for corresponding events and alarms. If the camera is seen as the human eye, the intelligent video surveillance system can be understood as the human brain. Intelligent video surveillance technology often utilizes the powerful computing capabilities of the processor chip to perform high-speed analysis on massive data in video frames, filter out information that users do not care about, and provide only useful information for the monitor.
The Generation and Characteristics of Video Intelligence Analysis
There are two basic problems that smart video systems need to solve: one is to free security personnel from the complex and tedious “eye-screen†task, and the machine completes this part of the work; the other is to quickly perform in massive amounts of video data. Search for the image you are looking for. According to statistical analysis, security operators will miss 90% of video information after staring at the screen video wall for more than 10 minutes, which will make this work meaningless. In the London Underground bombings, security personnel took 70 man hours to find the needed information on a large number of tapes. Therefore, based on the above two points, choosing a video analysis system will be able to free people from the arduous work and increase efficiency. The intelligent video analysis system will become the core component of the video surveillance system in the future.
The background of intelligent video analysis comes from the most basic requirements. For example, when security duty personnel face hundreds of cameras, they cannot really prevent or interfere with the risks. Most of them rely on playback-related video post-processing; Some non-security applications, such as business flow statistics, target recognition (license plates, faces, etc.), also require automatic intelligent statistical identification. Intelligent video analysis transfers the analysis and recognition of events to a computer or a chip, freeing the on-duty personnel from the work of the “staring†monitor. When the computer finds a problem, an alarm is generated and the on-duty personnel performs the corresponding processing operations.
The main advantages of intelligent video surveillance:
• Fast response time: Millisecond alarm trigger response time;
• More effective surveillance: Security personnel only need to pay attention to relevant information;
• Powerful data retrieval and analysis capabilities: Provides rapid response time and survey time.
Motion detection is the foundation
The vast majority of intelligent video analysis is based on the moving target detection technology, that is, the first intelligent analysis system can accurately complete the detection of the moving target, effectively separate the moving object from the background of the image, and extract the moving target information.
From the practical application of computer vision, the main challenges and problems that need to be solved in moving target detection, recognition, and analysis can be summed up in three aspects, namely, the robustness, accuracy, and real-time performance of the algorithm.
Robustness
Robustness is the robustness of the system to characterize the insensitivity of the control system to perturbations in characteristics or parameters. The robustness of the moving object detection algorithm is able to achieve continuous, stable detection, analysis and recognition of moving objects under various environmental conditions.
The most important reasons that affect the robustness of the algorithm are the following: changes in the state of the target, changes in the ambient light, partial irregularities caused by the target occlusion, and temporary disappearance of the target caused by all occlusion.
accuracy
The detection and recognition of moving targets are different for different applications, and their detection and identification rates are different. It is almost impossible to achieve 100% detection success, that is, there are false detections and missed detections. Because the actual monitoring scene environment is complex and ever-changing, there are a lot of noise and interference, optimization through the algorithm can improve the detection accuracy, and often can only be based on actual needs, in the false detection rate (false alarm rate) and missed detection. The balance between rates (missing rates) seeks balance.
real-time
A practical intelligent video surveillance system must have the ability to process video image sequences in real time. Because the processing method of video dynamic image is based on the processing of two-dimensional digital signals, the object to be processed contains a huge amount of data and information, requiring that the algorithm cannot be calculated too complicated and must be fast and real-time. For real-time analysis and warning tasks, the computational complexity is critical so that more resources can be allocated to more advanced tasks. However, real-time and robustness are often contradictory. How to seek balanced development is the key to technology.
In particular, it has been pointed out that currently the company has independently developed a video motion detection algorithm, which is less affected by the light and lens jitter compared to the classical motion detection algorithm, and has less computation time, which is more suitable for real-time product development. At the same time, the package development kit (SDK) is based on the autonomous algorithm and integrated in the company's digital security system software platform and network camera series products to achieve system-level and product-level reliable applications. At the same time, the project team is building video libraries for various scenarios extensively, testing the video for multiple scenarios using its own algorithms to better refine the algorithms and reduce the number of parameters that need to be adjusted to better meet the actual application requirements. .
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