Samsung Patents a Way to Find the Interesting Parts of Any Video Automatically
Most video search tools scan text descriptions or thumbnails, but Samsung's new patent goes deeper, reading the actual movement inside a video frame-by-frame to find the clips that matter.
How Samsung's video search spots the moments worth watching
A security camera stares at an empty hallway all night. So does a smartphone camera rolling in your pocket, or a dash cam on a long highway drive. The hard part isn't recording video; it's finding the two seconds you actually want.
Samsung's new patent describes a system that watches a video and figures out which frames are worth keeping. It does this by combining two types of analysis: one that tracks how things are moving across frames, and one that understands what those things are. Together, those two readings let it shrink a long clip down to the short burst that captures the action.
The result is a smaller, smarter slice of the original video that a search system can index and retrieve much faster. Whether you're digging through your camera roll or searching hours of security footage, the goal is the same: get you to the right moment without making you scrub through everything.
… extracting motion features based on the sequence of frame images and the optical flow information; extracting semantic features of at least one level of at least one frame image of the sequence of frame images; and determining a subset of frame images …
Translation: The system analyzes how objects move and what objects appear in the video to pick out the best parts.
How motion flow and scene meaning combine to filter frames
The patent describes a pipeline that processes a video in two parallel tracks.
Track one is optical flow, a technique that measures how pixels shift between consecutive frames. Think of it like drawing arrows on each part of the image showing where every object moved. Those arrows become motion features, numerical descriptions of the activity happening in the scene.
Track two is semantic analysis, which tries to understand what is actually in the frames: a person, a car, a hand gesture. The patent calls these semantic features, and they're extracted at multiple levels of abstraction so the system can recognize both fine details and broader context.
Once both tracks are complete, the system concatenates them (joins the two data streams end-to-end) and uses the combined signal to decide which frames are the most informative. The output is a subset of frames, a short representative clip pulled from the full sequence, with far fewer frames than the original video but carrying most of its meaningful content.
The practical application is video retrieval: when you search for something, the system compares your query against these compressed, meaning-rich clips rather than scanning every raw frame of every stored video.
Optical flow information is determined and used to identify a video clip from among a sequence of frames.
Translation: The software tracks movement across frames to figure out which video segments matter most.
What this means for search inside Samsung's video apps
For everyday Samsung device users, this kind of system could make searching your own videos feel less like hunting and more like asking. Instead of scrubbing a ten-minute clip to find the moment the dog jumps the fence, you'd ask, and the device would already know which frames to surface.
Samsung's steady investment in on-device video intelligence points toward tighter search inside the Galaxy camera roll and potentially security or dashcam apps. The efficiency angle matters too: by cutting a video down to its most informative frames before indexing, the system reduces the storage and processing load, which is meaningful when millions of clips live on a single device.
This is the 112th Samsung filing we've tracked since May in our camera sensor push watch, following one on saving clips with stills and one on remembering who you filmed.
The search technique described here runs entirely on software, which means no new camera hardware or sensors need to exist before this could ship. The phone's camera already captures everything the system needs.
The heavier lift is making the analysis fast enough to run in the background without draining a battery. A realistic first version probably runs overnight while the phone charges, building a searchable index of your videos the way a photo app already organizes pictures by face or location.
That framing puts this closer to a feature update than a research experiment, though turning a working idea into something polished enough for a mainstream gallery app still takes real engineering time.
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The drawings
38 drawing sheets from US 2026/0270506 A1 · click any drawing to enlarge
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