Video retrieval-analysis and management of new security data
Video retrieval is to find the required video clips from a large amount of video data. With the development of computer technology and network technology, video retrieval technology has been widely used in video on demand, digital TV, digital library remote education and telemedicine And other fields.
In the field of security, the construction of safe cities and smart cities in various places and the advancement of informatization in the field of public safety have all produced a large amount of surveillance video data. At the same time, the trend of high-definition and intelligent security industry development, the number of video data is exponential Level growth. However, video data is different from other traditional information data in the field of security. Traditional information in the past, after years of development of database technology, has formed a complete management platform based on information management system, which can quickly and accurately search and query the data of interest, such as population information management system, alarm platform, Vehicle management system, etc. However, video data currently does not have such an effective management system, and it is not possible to query and use the data that does exist conveniently and quickly. The development of video retrieval technology hopes to solve such problems.
In the field of security, video retrieval technology is mainly used in image detection. The role of video data in the detection of cases has also made graphic detection a new type of police with a large scale. The specific applications of video retrieval mainly include video enrichment and feature retrieval, and feature retrieval usually requires the support of intelligent analysis and image enhancement technologies.
The information density contained in surveillance video data is very low. For a specific demand, the useful data in 24-hour video data may only be a few seconds, but the information value of these few seconds of data is very high. Video enrichment technology is used to effectively increase the information density of video data. It will separate the moving objects of the video from the background, and then superimpose them on the background according to the time sequence and positional relationship to form a new video, which can maximize the degree of To retain environmental information while compressing useless information in time and space. Feature retrieval technology refers to indexing the features of the target in the video content, and then searching the video through the description of the features or the features displayed in the sample images to find video clips that match the features, quickly obtain more information from them, and improve efficiency To save labor costs.
Application and status quo-initial results, urgent need to be improved
Many security companies or related agencies in China have successively launched their own video retrieval systems. The specific work processes and principles are similar. The first step is to collect and centrally store the video data that needs to be retrieved for quick retrieval by the video retrieval system. Some manufacturers have developed special equipment to quickly copy the video data in the hard disk to improve the collection speed; the second step is to formally perform video retrieval. The first is to perform a concentrated summary of the data, and the concentrated video data is provided to the investigators. Manual inspection can also be used for feature retrieval; the second is to retrieve video data through features provided by the user.
Case handling personnel can greatly save the time for checking the video by viewing the concentrated video. When a suspicious target is found in the concentrated video, the corresponding original video clip can be quickly opened by clicking on the target on the video for detailed viewing. The case handler can further initiate the search by providing target features such as people or cars, target color, height, direction of movement, speed of movement, pedestrian dress, gait, or by providing a sample.
The video retrieval system first decodes the undecoded video data through an efficient decoder, then separates and extracts the moving targets through the background modeling algorithm, and then extracts and analyzes the focused features of these moving targets through the intelligent analysis algorithm. The analysis results are properly described, stored in the database, and compared with the requested search features. Finally, the highly relevant targets will be displayed as snapshots or other methods as results, and the original video clips can be quickly located.
Some manufacturers also analyze the basic characteristics of some moving targets, such as target category, color, speed and other information in real time during video recording, and use these characteristics to index the video content and store them in their own devices. In this way, when the feature retrieval is needed, only the feature provided by the user can be compared with the index information of the video content to locate the relevant video segment, and the video does not need to be processed and analyzed again, which can effectively save analysis time. However, at present, the standards of video content indexing and description of different manufacturers are not the same, which will cause the problem of system docking and video content index sharing between different manufacturers, and the system cannot fully play its role. MPEG-7 standardizes the description of audio and video features, gives guidance on solving this problem, and needs to further standardize and standardize the characteristics of security video data.
The video retrieval system currently in use in China can only index and retrieve some basic characteristics of the target. The current video retrieval system cannot complete the task of "finding a girl holding an iPhone 6" because of the resolution, Due to the influence of multiple factors such as clarity and video angle, we can't distinguish the model of the mobile phone, and we don't even know whether there is anything on the target; we can't even judge whether the target is a boy or a girl, because this era of rapid changes in popular factors . The amazing intelligent analysis effect shown in some film and television works can only provide researchers with some research ideas and directions.
In addition to being able to search for some of the basic characteristics of the targets mentioned above, what we can do now is to retrieve the license plate information, which benefits from the maturity of license plate recognition related technologies and the construction of intelligent transportation systems. For tasks such as face recognition, it is difficult to achieve high accuracy for general surveillance video, even with high-definition bayonet. It still needs to be combined with manual comparison to really play a role. The human eye can accurately distinguish and recognize However, the video retrieval system may not be able to achieve the goal of human beings, and it can achieve the resolution of the human eye. After all, it is the highest state and the ultimate goal in the field of machine vision. There is still a long way to go. Seeing the hidden propaganda of some products, the suspect in the video changed clothes, put on a hat, changed the vehicle, and the retrieval system can accurately retrieve the target. Forgiving the author's ignorance and ignorance, it is deeply doubtful.
We introduce a real case, which is currently the most conventional application mode of video retrieval technology in the field of security in China. In April 2013, a major masked robbery occurred in a place. After analysis by the task force, it was considered to be a carefully prepared and premeditated case. The suspect may have investigated the crime scene many times in advance. In order to solve the case as soon as possible, the local public security bureau's investigation team called for a week of various surveillance videos around the scene of the crime scene, including traffic surveillance videos, security surveillance videos, and own surveillance videos of nearby shops. Find suspicious targets and provide clues to solve the case.
Traditionally, it takes several days for several police officers to view the video at the same time. Through video enrichment technology, a police officer can screen out suspicious persons or vehicles in the video within a few hours. At the same time, the same criminal investigator completes the investigation It can also be more sensitive to the recurrence rate of suspicious persons in the picture, which saves the time for investigation and reduces the probability of omission. When suspicious vehicles are filtered out, the vehicle's driving trajectory is formed in the system through the license plate identification information, which provides a reference for the deployment control plan. Through the behavior of the vehicle, the area where the vehicle stays for a long time is located. , And then call the video around the area for summary investigation, combined with the suspicious targets in the crime scene, and finally locked the suspect.
The era of big data-opportunities and challenges of video retrieval technology
The construction scale of security infrastructure is constantly expanding, and the scale of real-time data has reached the level of PB. At the same time, video data from the consumer field is also providing data support for security. Everyone â€™s mobile phone may upload images to the public security platform. Information, provide clues to the case, to quickly and efficiently filter effective information from these massive information, video retrieval technology needs to be improved in two directions: first, the processing capacity and efficiency of video data need to be improved, to meet the level of massive data It processes data more accurately and quickly, extracts and describes the content of information, and performs efficient indexing and storage to meet subsequent data information retrieval, analysis, and mining operations. Secondly, it is necessary to improve the current data processing technology and storage architecture from the architectural level, and introduce new technologies for big data services to meet the retrieval needs of security data information in the big data environment.
At present, the distribution method of video data in the security field is mainly distributed and independently stored on the servers of all levels of institutions. The application of video retrieval is also mainly executed by an independent server. With the rapid expansion of data volume, this This approach is bound to encounter bottlenecks in data movement and computing power in the near future. The application of cloud computing and cloud storage technologies related to big data to the video retrieval service of security can solve the problem of data migration, and give full play to the advantages of distributed computing and discover sufficient computing power.
In the field of security, big data and video retrieval technology are actually promoting each other. Video retrieval technology provides technical support for the effective application of big data. Big data is also forcing the improvement of the efficiency, accuracy and coverage of video retrieval technology. These two technologies will be gradually upgraded and unified in the mutual integration.
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