Security camera manufacturers are developing AI gait recognition systems that identify individuals based on their unique walking patterns [1, 2].
This shift in surveillance technology addresses a primary weakness in current security systems: the reliance on clear facial images. By analyzing how a person moves, cameras can maintain a reliable identification stream even when a subject is far away or wearing a mask.
Digital Trends said a new AI gait recognition system can identify people by walking patterns, giving security cameras another long-range signal when faces are blurry, hidden, or too small to trust [1]. This capability allows the software to track individuals across different camera feeds without needing a high-resolution close-up of the face.
While gait recognition focuses on movement, other AI enhancements are arriving in hardware simultaneously. SwitchBot recently introduced the Outdoor Pan/Tilt Cam 3K, which records in 3K resolution [2]. The company said the device tracks motion, summarizes events with AI, and helps users find footage using simple prompts [2].
These advancements signal a move toward more autonomous surveillance. Traditional facial recognition requires specific angles and lighting to function effectively. Gait recognition, however, operates on the physics of a person's stride and posture, characteristics that are harder to disguise than a facial expression.
Developers are integrating these tools into both commercial security networks and home surveillance systems [1, 2]. As the technology advances, the ability to identify a person from a distance may become as standard as motion detection.
“AI gait recognition systems identify people by walking patterns.”
The transition from facial recognition to gait recognition represents a significant leap in surveillance persistence. By removing the requirement for a clear line of sight to a subject's face, security systems can track individuals more reliably across wide areas. This increases the efficiency of law enforcement and private security but also raises new privacy concerns, as walking patterns are nearly impossible to mask intentionally.
