AI for Security Algorithm List

Name	Description	Accuracy
Face Liveness	Using a non-cooperative near-infrared binocular liveness detection algorithm, it automatically detects and determines whether the face within the designated area of the image is a real, live face. If the algorithm identifies fraudulent attacks such as printed photos, screen playback, and 3D masks, it can immediately intercept and alert the management, prompting them to monitor and intervene. Additionally, it provides screenshots and short videos of the specific location for reference, ensuring the safety of personnel and property and reducing losses.	99%
Face Matcher	By extracting facial features from the images sent by the face detection module and comparing them with a database of known faces, it outputs a similarity score to determine an individual's identity. This system can also be used for facial surveillance, and if the similarity score exceeds a set threshold, it can immediately alert management. It enables quick identification of individuals within the database, enhancing security management efficiency and empowering public safety and personnel security management. It strengthens the control over individuals' illegal activities. If a person on the blacklist is identified, an immediate alert is triggered, prompting management to monitor and intervene. It also provides screenshots and short videos of the specific location for reference. This system can be applied in industries or scenarios requiring facial comparison for monitoring and surveillance, identity verification, access control, personnel search, and other related tasks.	99%
Face Capture	Automatically detect and locate the positions of faces within the designated area in videos or images, and transmit the captured and optimized facial images.	99%
Unknown Person Detection	By comparing with a known facial database, if the facial similarity comparison results do not exceed the set threshold within the algorithm's specified logic and a certain time frame, the person is identified as a stranger and an immediate alert is sent to management. This enhances security management efficiency, empowers public safety and personnel security management, and strengthens control over individuals' illegal activities. If a stranger is identified, an immediate alert is triggered, prompting management to monitor and intervene. Additionally, it provides screenshots and short videos of the specific location for reference, ensuring the safety of personnel and property, and reducing losses. It also provides management tools and value to the management department, improving management efficiency and reducing management costs. This system can be applied in industry scenarios requiring facial comparison for stranger monitoring and alerts.	99%
Head Detection	Head detection technology is based on deep learning algorithms that can automatically detect and track heads appearing in videos, returning corresponding position IDs, labels, scores, and location information. This technology is applicable in fields such as security surveillance, personnel access management, and advertising, helping to enhance personnel management and safety, reduce labor costs, and improve efficiency. When using this technology, it is essential to comply with relevant laws, regulations, and privacy protection policies to ensure its lawful and compliant use.	90%
Face Attributes	Automatically identify human attributes within a designated area of the screen, including the person's gender, age group, age, gender, mask, glasses, and hat.	92%
Human Body Attributes	Automatically identify human attributes within a designated area of the screen, including top style, top color, top texture, bottom style, bottom color, shoe style, shoe color, direction of movement, whether wearing glasses, whether wearing a mask, whether carrying a backpack, and whether riding a bicycle. This can enhance personnel safety and illegal activity control, improve security management efficiency, and provide data support or basis for subsequent management measures through statistical analysis of human attributes. It can be applied to various industries that require classification, identification, alerting, statistics, filtering, or searching of human attributes, thereby improving management efficiency and reducing management costs.	95%
Foot Traffic	Based on deep learning algorithms, track identified individuals in real-time video footage and count the number of people. Report the recognition results according to the established protocol.	92%
Human Detection	Detect human shapes in the frame to determine if there are people present. This can be used in scenarios such as passenger flow statistics, elevator occupancy counting, absence detection, and specific job role counting. It can quickly and accurately count the number of people in the frame, facilitating the generation of corresponding alarm events for business operations.	99%
Crowd Gathering	Detect the shapes of human bodies in the video footage, track and identify individuals in real-time video, and count the number of people. If the number exceeds the target, it can be used for traffic flow statistics and trigger corresponding alert events to assist business operations.	92%
Absence Detection	Based on deep learning algorithms, track and identify individuals in real-time video footage according to the configured absence detection zones and the required number of personnel. When the number of on-duty personnel does not meet the minimum standard, report the alarm results according to the established protocol.	95%
Crossing Detection	Based on deep learning algorithms, track and identify individuals in real-time video footage and detect their loitering behavior. Report the identification results according to the established protocol.	92%
Area Intrusion	Automatically detect if there is any intrusion in the designated area of the footage. If an intrusion is detected, it can promptly deliver a verbal warning to the intruder and alert nearby management personnel. It can also send real-time remote alerts to management for intervention and provide screenshots and short videos for reference. This system helps improve management efficiency, reduce management costs, ensure personnel safety, and minimize property loss. It is applicable in key monitoring areas of industries such as military, police, energy, communications, schools, forestry, mining, and warehouses. It is also commonly used in typically unoccupied or sparsely populated areas or during nighttime.	93%
Loitering Detection	Based on deep learning algorithms, track and identify individuals in real-time video footage and detect loitering behavior. Report the results according to the established protocol.	59%
Fall Detection	Automatically detect fall incidents within a designated area of the footage. Upon detecting a fall, it can promptly issue an alert or notification and provide the location coordinates of the fallen individual. This system helps in the early detection of falls, enabling timely emergency measures to protect the individual's safety and health, thereby improving quality of life. It offers more effective monitoring and care methods for relevant institutions and families. This algorithm can be applied in fields such as elderly care, medical monitoring, and smart homes to enhance quality of life and safety.	85%
Fight Detection	Automatically detect if there are physical conflicts or fights disrupting public order within a designated area of the footage. If such an incident is detected, it can promptly deliver a verbal warning to those involved and alert nearby management personnel. It can also send real-time remote alerts to management for intervention and provide screenshots and short videos for reference. This system helps improve management efficiency, reduce management costs, ensure the safety of personnel and property, and minimize losses. It is applicable in both indoor and outdoor scenarios where fight behavior needs to be controlled, such as schools, factories, warehouses, office areas, restaurants, and entertainment venues.	85%
Fire and smoke detection	Automatically detect the presence of open flames and thick smoke in images and provide relevant information such as the coordinates of the fire. This allows firefighting personnel or other relevant personnel to respond and handle the situation promptly. It helps in the early detection of fires, reducing fire damage and hazards. This system aids in improving management efficiency, reducing management costs, promptly identifying and replacing missing fire extinguishers, minimizing potential fire accident losses, and better protecting personal and property safety. It is applicable in fields such as fire safety and traffic control, and is currently suitable for charging scenarios such as electric vehicle charging stations and battery chargers.	90%
Passageway obstruction (scene anomaly detection)	Using visual algorithms, detect anomalies by comparing the current scene to a template (the background of the first frame when the algorithm is initiated is used as the current template and is continuously updated). Any area of pixels differing from the template by a certain adjustable threshold is considered an anomaly. This algorithm can be applied to scenarios such as passageway obstruction, environmental anomalies, and can assist in applications like road, bridge, and track safety inspections (fixed template).	90%
License Plate Detection	Detect License Plate
Safety helmet detection	Based on deep learning algorithms, track and identify individuals not wearing safety helmets in real-time video footage, and report the identification results according to the established protocol.	95%
Chef hat detection	Automatically detect whether kitchen staff are wearing chef hats within a designated area of the footage. This allows relevant personnel to promptly address any issues, achieving efficient supervision of kitchen staff and events, and reducing management costs. Additionally, it helps prevent hair and other contaminants from entering the food, mitigating food safety incidents and ensuring the health and safety of consumers and the public. This system is applicable in kitchen scenarios for restaurants, company canteens, schools, and similar industries.	90%
Chef uniform detection	Automatically detect whether kitchen staff are wearing chef uniforms within a designated area of the footage. This allows relevant personnel to promptly address any issues, achieving efficient and standardized supervision of kitchen staff, reducing management costs, and preventing unauthorized personnel from entering the kitchen. It ensures a clean kitchen environment, mitigates food safety incidents, and enhances security. This system is applicable in kitchen scenarios for restaurants, company canteens, schools, and similar industries. It can effectively improve the management efficiency and quality control level of chef uniforms.	90%
Smoking detection	The smoking behavior detection algorithm is an innovative technology based on machine vision, designed to monitor and identify smoking behavior in real time. By analyzing video streams or image data, this algorithm can accurately detect whether people are smoking, quickly and accurately identify smoking actions, and promptly issue alerts when smoking behavior is detected. Our smoking behavior detection algorithm offers high precision, real-time capability, and customization options. It can be applied in driving scenarios, providing effective monitoring and management measures.	85%
Phone call detection	An algorithm that utilizes computer vision technology to monitor and identify the misuse of mobile phones for personal calls during work hours by analyzing whether employees are using phones for personal calls during working hours. It aims to help employers or managers monitor employee behavior in real time, thereby improving work efficiency and productivity. This algorithm can provide data and evidence to help managers understand employees' work attitudes and behaviors and take appropriate management measures.	90%
Overflowing trash detection	Automatically detect whether there are overflowing trash bins or scattered garbage within a designated area of the footage. If such a situation is detected, the system provides the coordinates of the overflowing trash in the image, enabling relevant management personnel to respond promptly and address the issue quickly. This helps manage overflowing trash and scattered garbage, encourages proper disposal habits, reduces environmental pollution caused by bagged and exposed garbage, maintains a clean and tidy urban environment, and improves the quality of life for residents. It is applicable in scenarios such as urban or rural streets, neighborhood garbage stations, and community waste disposal points.	92%
Garbage detection	Automatically detect whether there are overflowing trash bins or scattered garbage within a designated area of the footage. If such a situation is detected, the system provides the coordinates of the overflowing trash in the image, enabling relevant management personnel to respond promptly and address the issue quickly. This helps manage overflowing trash and scattered garbage, encourages proper disposal habits, reduces environmental pollution caused by bagged and exposed garbage, maintains a clean and tidy urban environment, and improves the quality of life for residents. This system is applicable in scenarios such as urban or rural streets, neighborhood garbage stations, and community waste disposal points.	92%
Live object detection	By using visual algorithms to compare captured video frames, detect changes in objects and identify moving objects. This method does not rely on a fixed template.	85%

System-wise, all small model AI algorithm will be checked by multimodal large language model before sending to users. This significantly improves the performance.