What is Gait Recognition and How it Works

The gait recognition technology uses the camera to acquire, detect and segment the data of the walking process of the recognized target, that is, after the visual inspection of the entire walking process completes a completed walking cycle, extract the data for the features, and input the gait data. The database to be compared is compared to confirm the identity of the detection target identified. Compared with other biometric technologies, gait recognition has the advantages of non-contact long distance and not easy to camouflage.

Gait recognition technology can be divided into the following steps: collecting gait, analyzing pictures, feature extraction, and data comparison. First, the gait of the person is collected by cameras from different angles, and the video sequence of the gait is obtained through detection and tracking, and then the gait features of the person are extracted through preprocessing analysis. This includes the key processing in the early stage of gait recognition such as motion detection, motion segmentation, and feature extraction for the gait motion in the image sequence; secondly, after further processing, it becomes the same as the gait that has been stored in the database. Finally, compare and identify the newly collected gait features with the gait features of the gait database.

From a biological point of view, different people have different leg bone lengths, muscle strengths, heights of center of gravity and motor nerve sensitivity, which determine the uniqueness and stability of gait, so it is difficult to be imitated by others in a short period of time.

From the hardware point of view, gait recognition does not have high hardware requirements. Generally speaking, based on 1080p camera, its effective recognition distance can reach 50 meters. If the resolution reaches 4K high-definition configuration, its effective recognition distance Extendable to 100 meters. And it is a full-view recognition, no matter what direction people come from, they can recognize it. Gait features can be collected and recognized without the special cooperation of the recognizer.

However, in the practical application of gait recognition, it will be interfered by many factors, such as weather, obstructions, lighting, etc., which will make it difficult to recognize. Datatang has been committed to providing high-quality data to help our customers break down technical bottlenecks. Datatang strictly abides by relevant regulations, and the data is collected with proper authorization.

10,000 People — Simulation Monitoring View Gait Recognition Data

5,000 People — Gait Recognition Data in Surveillance Scenes. The data includes indoor scenes and outdoor scenes. The data includes males and females, and the age distribution is from children to the elderly. The data diversity includes multiple age groups, multiple time periods, multiple scenes, different camera angles, different seasonal clothes, different walk speed. The data can be used for tasks such as gait recognition in surveillance scenes.

100 People — Gait Recognition Data

100 People — Gait Recognition Data. The collecting scenes of this dataset include indoor scenes (exhibition hall, front desk), outdoor scenes (square, gate of the company) . The data diversity includes multiple age groups, multiple time periods, 5 kinds of scenes, 3 kinds of garments, different shooting angles. For annotation, the binarization processing was adopted for the image frames. The data can be used for tasks such as gait recognition in surveillance scenes.


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