Recently, Google released the AVA (Atomic VisualActions) database, which is composed of video collections exposed by YouTube. These videos can be annotated with 80 atomic actions, such as walking, shaking hands, kicking, etc. All actions have a space-time positioning, with a cumulative total of 57,600 videos. Fragments, 96,000 humanoid actions, and 210,000 action tags. Google said that the AVA technology released this time will help Google analyze video for many years, but also help advertisers better target consumers. Their ultimate goal is to teach computer social intelligence, which is to "understand what humans are doing, what to do next, and what they want to achieve."
At present, some domestic research institutes and enterprises have also invested heavily in related areas of behavior identification to solve the bottleneck problem of video surveillance data understanding. However, to achieve the ultimate goal of allowing the machine to “understand†humanity through actions, many efforts are needed. . "At present, most human behavioral identifications are still in the stage of motion recognition. For practical applications, recognizing human actions is still a huge challenge." On November 8, Professor Li Jinping, Director of Pattern Recognition and Intelligent Systems Institute, Jinan University Tell the Science Daily reporter. Move, the machine will find you? Experts said that at this stage, this is a "may have" thing.
application:
Security and other areas show great power
Li Jinping told reporters that motion recognition can be seen as a combination of feature extraction and classifier design. In elderly nursing homes, if an elderly person falls, the behavioral identification system can immediately alert the staff and even directly contact the hospital. The black technology is full of "Mission Impossible 5". The last line of defense in the security system is gait recognition... ... Despite the difficulties of technology, behavioral identification is still applied in related fields.
Professor Wu Fei, director of the Institute of Artificial Intelligence at Zhejiang University, told the reporter that this technology, which was first applied by Apple and Microsoft to the game, is also in the public security law (interrogation and care of detainees), electricity (wind power generation, safe production of the national grid), and banks. Multiple scenarios such as (business area intelligence security) and hospitals (patient status monitoring) have a wider application space. Especially in security and other fields, the application based on behavior identification is more extensive.
For example, gait recognition for identifying identities through human walking methods, although belonging to identity recognition, is also an effective crossover of behavior identification and identity recognition. Gait recognition is a kind of identity recognition through people walking postures. It is divided into four parts: human shape detection, segmentation, identification, and tracking. The most challenging part of these parts can not be separated from research based on behavior identification.
In the search for missing persons and suspected suspects to escape in recent years, gait recognition has played a significant role. In "Wisdom" program, the galactic drop technology successfully identified the suspect on the spot through gait. After increasing difficulty, it also identified the dog's silhouette by gait.
Huang Yongbiao, founder and CEO of Galaxy Drop Technology, said that gait recognition is used to realize abnormal behavior analysis, which is one of the important advantages of gait recognition compared to other biometric features. The proper meaning of anomalous behavior includes the ability to visually observe the target's body shape and motion state to find out whether there are abnormal behaviors. Usually, it involves the target detection, segmentation, key point location, identification, tracking and other technical links, and these technical links It is also the core underlying technology for gait recognition. At present, they have been able to complete long-distance uncontrolled pattern recognition.
difficulty:
Positioning, tracking, and scenario are difficult
“Difficulty in identifying behaviors is first and foremost manifested in the difficulty of locating and tracking human motion from continuous video streams.†Wu Fei said that in video behavior recognition, it is necessary to find a starting point and end point of continuous motions, that is, to continuously act from the video. Positioning in the stream is very difficult in video recognition. After finding the starting point of a coherent action, it is also difficult to track this action robustly. For example, a jump motion is not easy to achieve the continuous development of the tracking action, but also consider the complexity of the scene and the background dynamic changes or camera shake and other factors.
Not only that, scene recognition is also a big challenge. First of all, even if it is the same type of action, there are great differences between different scenes at different times. Wu Fei said that for example, different people walk at different times, and there are differences in speed, posture, and scene occlusion. This is especially the case for different types of actions. Li Jinping stated that "Unlike static objects, actions will develop over time and there will be more uncertainties." For example, a person's "running" photo may actually be just the person's jumping action. Only by seeing more pictures can it be possible to clearly know exactly what happened. It can be seen how complex the dynamics of people in the scene are. Therefore, the identification method of a certain action is difficult to directly use in the identification of another action. "One action, human identification two or three times can be, but to let the machine remember and identify, you need a lot of training data." Li Jinping said.
Compared with other datasets, AVA released by Google will provide people with in-depth research on relevant models by providing multiple tags with fine-grained temporal and spatial granularity in related scenes. Eventually, it will not only be able to achieve accurate human activities. Modules will also be further applied.
Outlook:
Equipment platform and theoretical model are in the ascendant
“In the area of ​​behavior identification, China’s development potential in the next five years and ten years is very strong. It is recommended that we tackle the issue of equipment and platforms. Nowadays, many domestic teams use equipment and platforms from foreign companies such as Microsoft. We hope that the country will in this regard. Give some support and policy guidance," said Li Jinping.
According to Wu Fei, China's scientific research teams and enterprises should also intensively develop theoretical models and data-driven basic theories. "The process of a behavioral action has to go through multiple states (corresponding to many time frames), and the human body also shows a different posture at each moment. Then, is it true that each frame is of the same importance in the action discrimination? The discrimination of motion is not exactly the same for each joint point in motion discrimination. These all require the improvement of basic theories such as the theoretical model,†said Wu Fei.
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