🔥 Trending AI Security Cameras: Identifying People by Their Walk

AI security cameras can identify people by their walk through a technology known as gait recognition. This advanced biometric method analyzes the unique way an individual moves, providing an alternative or complementary identification technique to more traditional methods like facial recognition ⁴⁹.

How Gait Recognition Works

Gait recognition systems utilize artificial intelligence, particularly deep learning algorithms, to process video footage from surveillance cameras and extract distinctive patterns from a person's walking style ⁵⁶.

1. Data Capture and Pre-processing

The process begins with security cameras capturing video footage of individuals walking. These cameras can be standard CCTV systems, often integrated with AI capabilities ¹²⁸. The captured video frames are then pre-processed to isolate the moving subject from the background. This may involve techniques like background subtraction and noise reduction.

2. Feature Extraction

Once the individual is isolated, the AI system extracts various "gait features." These features are the unique characteristics of a person's walk and can include:

• Body Posture: The overall alignment and stance of the body ⁴.
• Limb Movement: The swing of arms and legs, including their range of motion and velocity ⁶.
• Stride Timing and Length: The rhythm, duration, and distance of each step ⁶.
• Joint Bending: The angles and movements of knees, hips, and ankles during walking ⁶.
• Movement Dynamics: The overall fluidity and pattern of motion ⁴.

Unlike static biometrics such as fingerprints or facial features, gait analysis taps into the dynamic and behavioral aspects of an individual's movement ⁹.

3. Deep Learning Models

The extracted gait features are fed into sophisticated deep learning models, often neural networks. These models are trained on vast datasets of walking patterns from numerous individuals. Through this training, the AI learns to recognize subtle differences in gait that distinguish one person from another ⁵.

One example of such a model is SKDMap-Net, introduced in 2026, which focuses on these behavioral biometrics ⁶.

4. Comparison and Identification

When a new individual's gait is captured, the system compares their extracted gait features against a database of known gait patterns. If a sufficient match is found, the individual can be identified ¹⁰. The system assigns an "identity score" based on the similarity of the gait patterns ⁷.

Advantages of Gait Recognition

Gait recognition offers several key advantages, especially in surveillance scenarios:

• Distance and Angle Independence: Unlike facial recognition, which requires a clear view of the face, gait recognition can identify individuals from a distance, from various angles, and even when their face is obscured ⁴. This is particularly useful in crowded areas or when individuals are wearing masks or hats.
• Non-intrusive: It doesn't require direct interaction or cooperation from the individual being identified ⁹.
• Works in Low Resolution: While higher resolution is always better, gait analysis can be effective even with lower-resolution video feeds where facial details might be indistinguishable.
• Continuous Monitoring: Since walking is a continuous action, gait can be monitored over longer periods, providing more data points for identification.
• Integration with Existing Infrastructure: Watrix AI, for instance, integrates gait recognition with existing surveillance ecosystems, including AI cameras, video management systems, and cloud platforms ².

Applications and Future Outlook

Gait recognition is being deployed in various security and surveillance applications. Governments, for example, are actively exploring and deploying AI surveillance technologies in 2026, with gait recognition being a significant component ³.

Use cases include:

• Public Safety: Identifying suspects or persons of interest in public spaces ¹.
• Border Security: Monitoring pedestrian traffic at checkpoints.
• Access Control: Granting or denying access to restricted areas based on an individual's gait ⁷.
• Forensics: Analyzing video evidence to identify individuals involved in incidents.

While gait recognition is a more recent development compared to other biometrics, its potential for real-world surveillance applications is significant, with ongoing research focusing on improving its accuracy and real-time capabilities ⁵.