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Samsung · Camera · Filed Apr 15, 2025 · USPTO published Jun 11, 2026 · Jun 11 release

Samsung Patents a Way to Measure How Far Away a Pedestrian Is Using a Moving Camera

Most cameras can see a pedestrian. Knowing exactly how far away that person is — without a laser or a radar — is a much harder problem. Samsung's new patent tackles it with nothing but video frames.

By · Updated · 3 min read

Samsung Patent: Measuring Distance to Pedestrians via Camera, figure from US 2026/0162439 A1
Figure from the official USPTO publication.
Publication number
US 2026/0162439 A1(US20260162439A1)
Applicant
Samsung Electronics Co., Ltd.
Filed · Published
Apr 15, 2025 · Jun 11, 2026
Inventors
Yonggonjong PARK, Cheolhun JANG, Dae Hyun JI
Status when we published
Examiner raised objections; the company can respond
Document
18 claims
US classification
348/148
Examiner
PONTIUS, JAMES M (Art Unit 2488)
Parent application
Continuation of 17/518,762 (filed Nov 4, 2021)(17518762)

What Samsung's pedestrian distance system actually does

Imagine a security camera mounted on a car or a moving robot. It can clearly see a person walking across the street, but it has no depth sensor — no way to tell whether that person is 5 feet away or 50. That gap between "I see someone" and "I know how close they are" is a real safety problem.

Samsung's patent describes a method for closing that gap using only the video the camera is already capturing. As the camera moves, the system tracks specific body joints — shoulders, hips, knees — on the pedestrian across multiple frames. Because the camera's own position changes between each frame, the math can work backward to figure out how far away those body points must be in 3D space.

The clever part is identifying a static point on the person — a part of the body that isn't swinging or bouncing while they walk. Anchoring the calculation to something stable makes the distance estimate much more reliable, even when the person is in motion.

How the joint-tracking math produces a distance estimate

The system works in three main steps:

  • Pedestrian detection: The camera feed is scanned to find the region of the image that contains a person.
  • Static point selection: Within that region, the algorithm identifies a body part that stays relatively still relative to the rest of the body — useful because a walking person's limbs swing in ways that would throw off a simple measurement.
  • 3D joint reconstruction: The system extracts joint regions (think: anatomical landmarks like elbows, knees, and hips) and uses vectors — directional lines drawn through those joints — to compute their 3D coordinates (their position in real-world space, not just on the flat image).

The key insight is triangulation. Because the camera is moving, the same pedestrian appears at a slightly different angle in each successive frame. By knowing how much the camera moved between frames and where the static point appeared in each one, the system can calculate the actual distance — the same geometric principle a surveyor uses when taking measurements from two spots.

No dedicated depth sensor (like LIDAR or structured-light infrared) is required. This is a monocular approach, meaning a single standard camera is enough.

What this means for cameras that watch for people

Distance-to-pedestrian estimation is a core building block for driver-assistance systems, delivery robots, smart surveillance cameras, and any moving device that needs to avoid hitting people. Today, reliable distance measurement usually depends on LIDAR or stereo cameras — hardware that adds cost and complexity. A software-only solution that works on a plain video feed could bring that capability to lower-cost devices.

For Samsung specifically, this sits at an interesting intersection of its camera hardware business, its automotive components division, and its robotics push. A system like this could surface in dashcams, in-car ADAS processors, or robot vision modules. You might eventually benefit from it indirectly — as a pedestrian who's less likely to be struck by a vehicle whose camera finally knows how close you are.

Source: US 2026/0162439 A1 at the USPTO (PDF). Full patent text and figures are in the official publication.

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