Behavior analysis AI is a technology that allows AI to analyze human behavior patterns over time based on skeletal structure and posture. For example, when a person falls, AI automatically detects that the person has fallen, or detects dangerous behavior that leads to a fall based on behavior patterns.
There is no limit to the number of actions that can be described as "behavior" in a single word. Asilla picks up security-related behaviors such as "abnormal behavior (falls, strokes, fights, and breakage)" and "suspicious behavior (staggering and uncomfortable behavior)" and is developing its own algorithm.
Asilla has been focusing on behavior recognition AI since 2017 and has been developing an AI security system since 2022. After the introduction of the system, we have been receiving an increasing number of requests from our customers, in addition to their initial needs, asking if the system can detect more detailed behavior, and recently we have been expanding the system to the retail domain in addition to the security domain.
In order to fulfill our customers' requests, we are currently conducting research and development focusing on even more detailed movements.
- What exactly do you mean by detailed actions?
Lets take shoplifting as an example. Shoplifting is the act of "actually taking something," but it is often preceded by a combination of various actions, such as going back and forth across the sales floor for an extended period of time or engaging in suspicious behavior such as scurrying around to check one's surroundings.
We believe that the range of detection will be further expanded if we can detect minute movements of people, such as a slight movement of the hand or scurrying, and assemble multiple actions, rather than obvious movements such as falling or entering.
A human perspective is required in order to accumulate detection of subtle behaviors and derive detection results. As mentioned above, even if each individual behavior can be detected, correct detection cannot be achieved without understanding human behavior and then guiding the behavior in a composite manner.
In order to detect such things as predictive behavior, it is first necessary to identify what kind of behavior precedes the target behavior, and then break it down into detailed behaviors that may occur. In addition, these behaviors need to be learned and defined by AI.
Let us illustrate this with a concrete example. For example, in the case of "shoplifting prediction," as described above, we need to identify what kind of behavior constitutes shoplifting in the first place. In this process, the AI will decipher specific examples such as "wandering around the sales floor more than necessary" or "paying more attention than necessary to the movements of store clerks" as signs of shoplifting behavior, and further define the behavior. It learns the minute movements of specific behaviors, and based on the complexities of these behaviors, it is able to "detect predictive signs of shoplifting.
In this way, when learning minute behaviors and detecting the signs of behavior that is the accumulation of such behaviors, there is always the question of "do people always behave in such a way? In this respect, a human science perspective is needed to define the behavior.
Learning -accurate AI requires "quality data" and "appropriate algorithms," and a human science perspective is also essential for both.
HSAR is conducting research and development with highly practical technologies for use in the real world in mind, and is building a proprietary behavioral database to maintain its high accuracy. The key point of this unique database is that it is constructed from a combination of "business perspective" and "human science perspective.
For example, training data in research is automatically generated by generative AI. AI cannot understand whether this data is "an inherently human behavior" or not. Therefore, it is necessary to make a correct judgment from the viewpoint of human science using psychology.
In the case of Asilla, the generative AI not only generates the original data, but also acquires actual human behavior data and uses it in a composite manner. In this way, the quality of the data itself is improved by reflecting actual human data, rather than processing AI alone, and the quality of the behavioral data generated by AI is also improved, thus promoting research from the bottom up.
HSAR also incorporates a human science perspective in the design of algorithms, which play a very important role in the AI learning process.
Since AI is not all-powerful, in some cases it may not be able to recognize differences in human behavior and create the correct algorithm.
For example, a "fall" is a behavior that occurs over a short period of time, a few seconds, whereas a "wobble" is a behavior that occurs over several seconds, right? A person who is familiar with human behavior can correctly understand the characteristics and differences of such movements, but an AI may not necessarily understand and judge them correctly.
In addition, it is very important to design an algorithm that takes into account universal and physical characteristics of humans, such as "the right hand and right elbow are connected, so they behave regularly.
When AI is not learning correctly, we point out the error to it from a human science perspective and try to improve it to promote correct understanding. We are trying to improve the AI to promote correct understanding.
Thus, we believe that it is necessary to combine human science methods, such as behavioral psychology and criminal psychology, to improve the accuracy of AI without the involvement of humans with a deep understanding of human behavior.
The ultimate goal of Asilla's products is "to prevent incidents and accidents before they happen and realize a safe and secure world.
In order to realize this world, it is essential to improve the accuracy of AI to achieve the realization of danger prediction.
The key to realizing "prediction of danger" is the accurate detection of even the smallest behaviors and the ability to identify potential risks through the combination of these behaviors. We are dedicated to advancing our research on behavior analysis to a level where AI can effectively detect even the slightest behaviors, make accurate determinations, and be applied across a wide range of real-world products, including Asilla.
Based on the philosophy of "Technology Driven Future," Asilla conducts research and development on a daily basis in order to realize a world in which all people can live safely and securely. In the field of behavior recognition AI, in particular, we have a strong desire to become a world leader, and our researchers and members are working together as a team to constantly evolve.
In the future, we will further promote the further evolution of our behavior recognition AI technology and strive to create an environment where all our engineers can pursue their own careers and always be excited about their work. If you are interested in AI related to human behavior, we look forward to working with you to create something together.
Inquiries about HSAR: email@example.com Contact: Wakasa
After completing his postgraduate studies at Tokyo Institute of Technology, he worked for JGC Corporation where he was involved in plant design IT work on overseas construction projects. He then joined Asilla Corporation, where he was in charge of proof-of-concept and product development projects related to behavior recognition AI, and became the company's executive officer and CTO in 2022. He continues to focus on product development using AI technology and research and development of new technologies.