Verification of whether it is a real person or a dummy is carried out using silent live body technology, which is recognized as very difficult in the industry. It uses both single RGB and binocular cameras to detect whether it is a real person or not, and can effectively block attack materials, including 3D simulated head models, paper-printed photos and displays. The algorithm is certified by UnionPay Live Enhanced.

Using an ordinary camera, faces can be reconstructed quickly and with high accuracy. Its 3D reconstruction technology can restore the 3D attributes of the face from a single or multiple images and can be used in metaverse scenarios such as virtual character creation and virtual clothing.

In a dense crowd scene, head and shoulder tracking can efficiently count the flow of people. It can also respond in a timely manner to some anomalous gatherings, stagnation and other events. On edge devices such as Hesse and ARM, its response is near real-time.

The pedestrian detection algorithm is responsible for detecting the location of pedestrians in images, including scenes such as neighborhoods, shopping centers and schools. It is significantly superior in rainy and foggy days, at night and in other poor weather conditions, with a combined detection accuracy of 99.8%.

Crowd Density Counting is used to estimate the number of people on screen in crowded scenarios, such as railway stations, airports, tourist attractions and other key surveillance areas, to count the current density of crowds and sudden increases in crowd size.

Pedestrian re-identification is an algorithm that uses computer vision to determine the presence of a specific pedestrian in an image or video sequence. First, an image of a pedestrian is selected and fed into the algorithm. Then, the image of that pedestrian is retrieved under the cross device to compensate for the visual limitation of the fixed camera, so that the trajectory of activity can be effectively tracked across cameras. This algorithm can be combined with pedestrian detection/tracking technology and is therefore widely used in intelligent video surveillance, intelligent security and other areas.

Pedestrian attribute recognition has a wide range of applications in pedestrian search, user profile analysis and intelligent surveillance. Based on big data and deep learning techniques, the algorithm can detect multiple targets in complex scenes and recognize 130 fine-grained categories among 15 pedestrian attributes, including gender, age, head accessories, hairstyle, hair color, upper and lower clothing, and emotion.

Pedestrian Skeletal Key points are used to locate the head, neck, left and right shoulders, left and right elbow joints, left and right wrist joints, left and right hip joints, left and right knee joints, and left and right ankle joints of the human body to provide data that can be used for interactive games and to detect abnormal movements such as falls. The algorithm can run on the Hesse series of AI chips or on low-end ARMs.

For a specified area, events are detected when a pedestrian enters the area for more than a specified amount of time. If certain thresholds are exceeded, an alarm is triggered.

For a specific area, events are detected when pedestrians loiter or linger in the area for more than a certain amount of time. If certain thresholds are exceeded, an alarm is triggered.

The algorithm of head and shoulder detection is used to count pedestrians entering a detection area. If the count threshold is exceeded, an alarm is triggered.

The algorithm of pedestrian detection and tracking is used to detect pedestrians entering the detection area for falls and to alert pedestrians exceeding certain thresholds for falls.

Pedestrian tracking and smoking detection algorithms are used to detect the smoking of pedestrians entering the detection area, and pedestrians exceeding the threshold are warned of their smoking behavior.

Pedestrian tracking and phone call detection algorithms are used to detect phone calls from pedestrians entering the detection area, and phone call behavior alerts are provided for pedestrians exceeding the threshold.

This algorithm is used to distinguish whether people are wearing helmets, hats or ordinary hats in construction sites, factories and other scenes, providing a powerful monitoring and warning function for safety production. It can achieve very high accuracy even in complex lighting scenes.

The application uses a pedestrian detection algorithm to provide early warning of non-helmet wearing behavior.

The algorithm detects whether site personnel are wearing reflective clothing and alerts them if they are not.

Head-and-shoulders or pedestrian detection algorithms detect when people are off duty. The number of people and time in the area is calculated and a warning is issued if it is less than the required number of people and time.

The algorithms of pedestrian tracking and unmasked detection are used to provide early warning of non-mask wearing behavior.

The algorithms of pedestrian tracking and chef’s clothing detection are used to determine if all the overalls and hats are in compliance, and to raise an alarm if they are not.

The algorithms of pedestrian tracking and phone play detection detect phone play for pedestrians entering the detection area and alerts pedestrians who exceed a threshold for phone play behavior.

The fight detection algorithm provides early warning of fighting behavior.

The running detection algorithm provides early warning of running behavior.

The AI Fine Behaviour Recognition algorithm is used to automatically monitor, intelligently record and alert on protective clothing removal behaviours in hospitals, airports, quarantine stations and other locations. Each key action of the inspected personnel in the process of removing protective clothing is detected for action standardisation, action duration monitoring and step accuracy detection.

The algorithm for detecting objects thrown from tall buildings is used to capture overhead throwing violations in real time by clipping video from massive video data at the time before and after the overhead throw, which is used by the property management department for evidence collection and traceability. The algorithm can be run in Huawei's SDC smart cameras without the need to configure additional analysis equipment, and has good anti-interference capability for day and night, with a comprehensive capture rate of 95% *.

The algorithm is used to detect electric vehicles entering lifts and buildings, and to prevent illegal indoor charging of electric vehicles, thus preventing fires caused by indoor charging of electric vehicles. The algorithm can effectively distinguish more than 80 types of vehicles, including electric vehicles, bicycles, children's toy cars, scooters, prams, etc., with a combined recall rate of 95% for electric vehicles.

The smoke detection algorithm detects smoke in the area and alerts on events that exceed a threshold time.

Flame detection uses real-time RGB and IR surveillance cameras to accurately detect open flames and smoke in the monitored area, while effectively controlling the frequent occurrence of false alarms.

Fire occupancy detection records the time a vehicle spends in a specific area to determine if there is an illegal line occupying the road so that the occupying vehicle can be captured and alerted.

Rubbish detection is used to detect litter such as pieces of paper, drinks bottles, plastic bags and other common litter.

Trash overflow detection detects when nearby bins are overflowing.

The mouse detection algorithm in infrared scenes detects the presence of mice in the area and alerts on events that exceed a threshold time. Note that this must be in a night infrared scene.

The non-motorized/motorized inspection algorithm detects motorized and non-motorized vehicles entering the detection area and alerts motorized and non-motorized vehicles that exceed the threshold time.

The algorithm achieves a accuracy of 90% by detecting illegal occupancy in public areas such as cities and streets, including food stalls, private vendors, carts and ground stalls. Note that this data is under the standard experiments of Minivision Tech.

The non-motorized/motorized vehicle inspection is used to monitor illegally parked vehicles or vehicles moving illegally on the road, but can also be used for other tasks such as number plate recognition. Even at night, the infrared devices maintain good detection accuracy for illegally parked motorized vehicles.

Engineering vehicle detection is used to detect dump trucks, cement tankers, excavators and other construction vehicles on construction sites or in restricted urban areas to prevent them from entering prohibited work zones or areas.

Dump trucks should have their wheels cleaned as required before leaving the site. The algorithm in the cleaning process to determine whether the site in accordance with the requirements of the vehicle spray cleaning.

River Float Detection primarily detects floating weeds, aquatic plants and debris in lakes, rivers, ditches and other scenarios with 98% accuracy.

It detects the hydrological scale through the surveillance camera and applies deep learning algorithms to the scale. It is applicable to scenes such as rivers, ditches, lakes, etc., as well as a wide range of hydrological scales without specific limitations.

Quality control and defect detection are used to detect abnormal defects such as scratches and dents on the surface of parts and whether parts are installed.

Adversarial Generative Networks, Portrait Segmentation and Portrait Detection algorithms are used to create an AI creation from uploaded images that can transform real-world photos into cartoon styles with beautifully natural results. It can be used for motion graphics and AI photo albums.

This algorithm reproduces the full expression of the character in the video on the photo of the character uploaded by the user, so that an impromptu short video production can be achieved from a video and a photo. Due to its extremely low computational cost, it can be used on platforms such as CPU and GPU to facilitate user call.

This algorithm uses an adversarial neural network to add fine detail to a sketch and create a realistic landscape photo, with one-click support for switching between the four seasonal styles and applying novel filters to the image.

By extracting features from faces and nose prints, the similarity of two dog faces is calculated to determine if they are the same dog. Through creating an exclusive ID for each dog that can record the dog's basic information, eco-dynamics, consumption information, service information and medical information, owners can be helped to better manage their dog's daily affairs.

The deep learning algorithm predicts the current category of the dog's status based on the leash, distance between human and dog, and behavioral actions. These categories include leash compliance, leash too long and unsigned leash. The algorithm issues an alert when a dog is off leash.

OCR can be used to recognize specialized documents such as ID cards, marriage certificates, invoices and other documents. It has an accuracy rate of 97% for text recognition in general scenarios.

License plate recognition can be used with a variety of number plates including yellow, green, blue and white. It has good stability in complex day and night environments such as roads, residential areas and access gates, and has a recognition accuracy of up to 97%.