Video analysis has become widely utilized due to the spread of AI technology. Technology where AI processes and analyzes video data to automatically detect people, objects, and more offers benefits such as reduced work time and effort, as well as improved productivity.
While some may be considering implementing it in their own company, it is not recommended to simply adopt it just because it "seems convenient." Many people likely have only a vague image of video analysis, which is a cutting-edge technology.
It is necessary to consider whether video analysis is the optimal technology by comparing it with your objectives and the challenges you face. Practical application is difficult without understanding in what situations it is used and what the points to consider during operation are.
Therefore, this article explains the mechanisms, analysis methods, benefits, use cases, and points to consider regarding video analysis. Through this article, you should be able to deepen your understanding of video analysis and specifically imagine operations after implementation. Please use this as a reference.
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Video analysis and video analytics refer to technology that analyzes visual data with a computer to identify and specify specific information. Through video analysis, it becomes possible to automatically identify actions, objects, human behavior, and more within a video and record them as data.
Since visual data is vast, manual analysis requires significant time and effort. Therefore, efficient and accurate analysis is possible by using AI.
Video analysis is applied in various fields such as sports, medicine, autonomous driving, and security. It is expected to obtain new discoveries and insights after significantly streamlining the observation and judgment processes that humans have traditionally performed visually. Video analysis can be said to be a technology that will increase in importance in the future.
Since video is a sequence of images, video analysis is performed through a sequence of image analyses.
Internal Link: "What is image recognition? Types, mechanisms, AI development processes, case studies and key considerations."
In video analysis, video data is first collected. It is necessary to obtain video data from various devices such as cameras, smartphones, and drones and secure them as a dataset.
Since collected video data is often not suitable for analysis as it is, preprocessing is required. Tasks such as noise removal from footage, frame extraction, and resolution adjustment are performed.
Once video preprocessing is complete, video data is analyzed using methods such as the following:
By combining the above analysis methods, it is possible to extract diverse information from videos and perform accurate analysis. Recently, analysis accuracy has improved significantly through the utilization of AI algorithms, especially deep learning. This enables more accurate information extraction and decision-making.
By implementing AI-based video analysis technology, benefits such as the following can be expected:
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By understanding the benefits, you will be able to properly judge whether the implementation of video analysis is necessary for your company.
One benefit of AI-based video analysis is time reduction through automatic processing. High-speed processing using AI enables real-time video analysis.
While manual video analysis by humans requires vast amounts of time and effort, implementing AI technology makes it possible to process these tasks quickly and accurately.
For example, with an Edge AI camera equipped with AI, detection of abnormal behavior through automatic analysis becomes possible on a 24-hour basis, and the time required for human processing is significantly reduced. Long-term monitoring work becomes unnecessary, allowing focus on rapid response during emergencies.
Automatic processing by AI is effective for operational efficiency in addition to saving time.
Because AI can efficiently process large amounts of video data, large-scale data analysis that is difficult for humans becomes possible. Furthermore, comprehensive analysis combining video data with other types of data (audio, text, sensor data, etc.) becomes possible.
Moreover, it is possible to find complex patterns and correlations from vast amounts of data that are difficult for humans to notice, enabling deeper insights and predictions. Furthermore, wider-range and long-term trend analysis and anomaly detection can be realized.
AI-based video analysis contributes to the optimization of human resources. By utilizing AI, processing work is automated, making it possible to significantly reduce the human burden.
This allows for the optimization of limited human resources. The number of people required to perform video analysis becomes clear, enabling the appropriate placement of personnel who were in excess or shortage. Productivity improvements can also be expected through improved personnel allocation.
Practical application of video analysis is progressing, and there are use cases such as the following:
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There are cases where video analysis technology is utilized for defect detection on manufacturing lines. Cameras equipped with video analysis systems monitor manufacturing process footage in real-time to detect minute defects or failures. Defective products are eliminated before they enter the market, making it possible to maintain product quality.
For example, on an electronic equipment manufacturing line, the position of components attached to a board and the state of soldering are checked with video analysis. Even for minute defects that tend to be missed by the human eye, AI can instantly detect them from the video and issue correction instructions without stopping the line.
Manufacturers can improve the precision of quality control while also realizing production efficiency improvements and cost reductions.
Video analysis enables warehouse inventory management and remote inspections. Through video analysis technology, the status of inventory in a warehouse is monitored in real-time, preventing inventory shortages or excesses and misplacements, and supporting replenishment and shipping at appropriate times.
By using cameras mounted on drones, it is also possible to patrol shelves in a warehouse, automatically read product labels, and update inventory data. Drones are effective for inspecting vast warehouses or high shelves, significantly reducing manual labor.
Furthermore, proposals for maintaining optimal inventory levels can be made based on inspection results and past data. It is possible to predict replenishment amounts and reduce wasteful inventory costs.
At building sites, video analysis supports the monitoring of construction progress and safety. Through video analysis, it is possible to ensure an efficient and safe working environment.
By implementing video analysis in cameras installed at construction sites, the movement of workers and the operational status of heavy machinery are monitored, instantly detecting behavior that violates safety standards or dangerous situations. Detection results are notified in real-time, enabling rapid response. Also, monitoring progress status prevents construction delays and supports execution according to the schedule.
Furthermore, it is possible to use drones to perform inspections of high places or wide areas. By analyzing footage taken by drones, the structure of buildings and the quality of finishes can be checked, and if there are problems, they can be discovered and repaired before handover.
In the agricultural field, it is utilized for the management of agricultural crops. It is possible to check the status of crops in real-time and provide data for maintaining an optimal cultivation environment.
In vast areas of farmland, there are limits to human visual inspection. Therefore, by using drones or fixed unmanned cameras equipped with video analysis technology to capture footage of the entire field, leaf color, shape, and growth rate can be automatically analyzed in real-time. Early discovery of crops that are not growing or pests also becomes possible.
Furthermore, video analysis is useful for harvest period prediction and quality control. By determining maturity and predicting the optimal harvest time, it will be easier to harvest agricultural crops at ideal quality.
Farmers can utilize resources efficiently and stably produce high-quality agricultural crops.
Video analysis technology can be utilized for the discovery of suspicious persons or blacklisted users. It is possible to quickly specify suspicious behavior or specific individuals at stores, venues, etc.
For example, by using video analysis technology for security cameras, blacklisted users who have caused problems in the past or individuals with criminal records are detected. AI instantly analyzes face and posture features and compares them with a database, making it possible to identify target individuals. Additionally, through behavioral analysis technology, it is possible to build systems that detect abnormal movements or behaviors and issue alerts when suspicious actions are confirmed.
Furthermore, video analysis technology is utilized in public places and large-scale event venues. Individuals can be specified even in crowds, and suspicious or dangerous persons can be tracked. Video analysis can be said to be effective from a security aspect as well.
Video analysis technology utilizing AI can be used for attendance management and entry/exit management. Video analysis is also utilized in systems that automatically record employee entry and exit through face recognition, automatically recording accurate clock-in and clock-out times.
Also, unauthorized entry/exit can be detected in real-time, and rapid response can be taken if an unauthorized individual enters a facility.
Previously, there was management through entry on paper, but video analysis makes it possible to build automatic recording and management systems.
When putting video analysis into practical use, it is necessary to pay attention to the following three points:
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By performing video analysis while paying attention to these, efficient and accurate analysis is possible. We will explain each point.
In video analysis, it is very convenient for AI to process data automatically, but taking processed data at face value should be avoided. Analysis results from AI may include false detections or oversights, and checking with human eyes becomes necessary.
Let's take footage analysis of security cameras in a store as an example. Humans have habits and routines, and there is a possibility that the AI may mistakenly detect them as abnormal behavior. Therefore, a human must ultimately judge whether that person is suspicious.
In this way, by creating a double-check system with AI and humans, false detections and oversights in video analysis can be prevented. Let's keep analysis results only as a reference and have humans make the final judgment.
To perform video analysis effectively, it is important to utilize video analysis AI or software suitable for the video data being processed. The optimal AI differs depending on the type of video and the purpose of analysis. Therefore, it is possible to enhance the precision and efficiency of video analysis by utilizing appropriate AI.
For example, when analyzing high-resolution footage, it is necessary to utilize AI with high processing capabilities. For real-time video analysis, AI with fast processing speed capable of edge processing, etc., is necessary. Also, if specific analysis functions such as face recognition or action detection are required, choose AI specialized for those purposes.
High-quality video annotation is indispensable to enhance the precision of video analysis. Video annotation refers to the task of attaching labels to specific objects or actions within video data. Through video annotation, AI models can learn accurately from datasets, making it possible to enhance analysis precision.
When identifying individuals in surveillance camera footage, it is necessary to perform analysis by attaching accurate labels to the person's face and body parts. At that time, if the precision of video annotation is not high, the AI model will learn incorrectly, which becomes a factor in lowering the precision of analysis results.
To ensure the quality of video annotation, it is necessary to annotate various datasets under the same conditions. This allows consistent video analysis to be performed and can enhance analysis precision.
Video analysis is expected to be utilized in industries where labor shortages are serious, such as manufacturing, logistics, and agriculture. There are already implementation cases, and it can be said to be a technology that will spread further in the future. It is recommended to acquire the basic knowledge introduced in this article.
If you are implementing video analysis technology, improve the quality of video annotation. That said, since specialized knowledge is required for the improvement and quality maintenance of annotation, it is recommended to outsource to a company specializing in annotation. Please consider doing so.