The system is different from traditional face recognition and ID match. It enables simultaneous face-tracking acquisition and face attribute analysis feedback results. It acquires holographic high-density face data of the subject at the front end: attributes, appearance, features, collection time, geographical location, and other essential information. These features can be identified and distinguished. WiMi's system combines edge computing, AI arithmetic acceleration, deep learning algorithms, holographic data gain technology, convolutional neural networks, face recognition, and acquisition to merge and upgrade the existing security video system.
WiMi's system, which uses a time window for the sampling period, can also be set up according to specific targets, such as critical positions, time on duty, and geographic space. If multiple samples are in a sampling period, the system will select the best sample as the last sampled information. The system will recognize all faces if multiple faces appear in a sampling frame. For completely unrecognizable looks, the system adopts an ignore-and-remain strategy. It focuses on capturing them again in subsequent frames or other surveillance cameras until they are recognized, thus ensuring full recognition and data integrity and keeping the area safe.
This system can provide structured primary data for security management and production safety by matching high-density dynamic personnel information collection to surveillance video of key locations.
The system includes a video access port, a hologram decoding and frame extraction module, a hologram optimization module, an edge computing and algorithm acceleration module, a face acquisition and analysis module, central control module, and a data storage and notification module.
Video access port interfaces with existing surveillance video. Hologram decoding and frame extraction module deals with the frames according to the central control module. The hologram optimization module performs image acquisition and analysis in the extracted frames, image optimization acceleration of the sampled frames, and sends feedback to the central control module. If information gets lost, new instructions will be issued by the central control module to reproduce information. With ARM architecture, the edge computing and algorithm acceleration module contains core computing units. Embedded with multi-layer CNNs, the computing unit performs algorithmic operations on low and high parallel computing performance. The face acquisition and analysis module collects recognition, segmentation, and extraction data by combining photos, geographic information, and time information. The collected face information is analyzed for attributes, gender, age, ethnicity, masks, and glasses. The central control module realizes the sampling process for management, integrated control, and management of other modules. The data storage and notification module stores the collected personal information locally and can notify external systems according to the information level.
The system's front end accesses the video through a dynamic holographic face recognition algorithm based on edge computing, decodes the video holographically, and detects, tracks, captures, and de-emphasizes the faces in the picture. The system uses feature values as information identifiers to build information on pedestrians, completing the collection of information on people and enabling private domain management to improve information security levels. In addition, the system is easy to deploy. It can be deployed in various ways, including external, rack-mounted, and mobile. The device can be directly connected to existing HD network cameras. It can be used directly on the front end to complete part of the video structuring work, obtain high-quality face-structured data, and improve the speed and calculation efficiency of back-end intelligent identification and analysis, making full use of the existing stock of cameras. Existing unstructured video can be directly upgraded to smart structured data through external attachments.
WiMi's edge computing-based holographic face recognition AI chip system can be used in various essential situations as it can perform high-density dynamic holographic face capture in complex environments. The system uses a digital camera with an intelligent front-end for face information collection and can meet a wide range of requirements for security information collection.
About WIMI Hologram Cloud
WIMI Hologram Cloud, Inc. (NASDAQ: WIMI), whose commercial operations began in 2015, is a holographic cloud comprehensive technical solution provider that focuses on professional areas including holographic AR automotive HUD software, 3D holographic pulse LiDAR, head-mounted light field holographic equipment, holographic semiconductor, holographic cloud software, holographic car navigation and others. Its services and holographic AR technologies include holographic AR automotive application, 3D holographic pulse LiDAR technology, holographic vision semiconductor technology, holographic software development, holographic AR advertising technology, holographic AR entertainment technology, holographic ARSDK payment, interactive holographic communication and other holographic AR technologies.
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SOURCE WiMi Hologram Cloud Inc.
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