Object detection in videos
AssigneeMorphoTrust USA, LLC
InventorsBrian K. Martin, Brian Bertan
Life of this patent
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Abstract
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving a video feed of a scene. Selecting a first portion of the video feed and a second portion of the video feed based on a probability of an object being present in the first portion of the video feed compared to a probability of the object being present in the second portion of the video feed. Processing a first portion of the video feed using a first detection algorithm to detect the object in the first portion of the video feed. Processing a second portion of the video feed using a second detection algorithm to detect the object in the second portion of the video feet, where the first detection algorithm is different from the second detection algorithm.
Claims15
A computer-implemented method for selectively applying multiple object detection algorithms, the method being executed by one or more processors and comprising:
- receiving, by the one or more processors, a video feed of a scene;
- obtaining, by the one or more processors, a probability model of the scene in the video feed, wherein the probability model is based on historical detections of an object within the scene;
- determining, by the one or more processors, a probability of the object being present in a first portion of the video feed and a probability of the object being present in a second portion of the video feed based on the probability model of the scene in the video feed;
- selecting, by the one or more processors, the first portion of the video feed and the second portion of the video feed based on the probability of the object being present in the first portion of the video feed compared to the probability of the object being present in the second portion of the video feed;
- wherein the probability of the object being present in the second portion of the video feed is greater than the probability of the object being present in the first portion of the video feed;
- processing, by the one or more processors, a first portion of the video feed using a first detection algorithm to detect the object in the first portion of the video feed;
- and
- processing, by the one or more processors, a second portion of the video feed using a second detection algorithm to detect the object in the second portion of the video feed,
- wherein the first detection algorithm is different from the second detection algorithm, and
- wherein the probability model includes a spatial probability model, that maps probabilities of detecting the object to regions within the scene, and a temporal probability model that maps probabilities of detecting the object to time periods.
A system for selectively applying multiple object detection algorithms, the system comprising:
- one or more computers;
- and
- a computer-readable medium coupled to the one or more computers having instructions stored thereon which, when executed by the one or more computers, cause the one or more computers to perform operations comprising:
- receiving a video feed of a scene;
- obtaining a probability model of the scene in the video feed, wherein the probability model is based on historical detections of an object within the scene;
- determining a probability of the object being present in a first portion of the video feed and a probability of the object being present in a second portion of the video feed based on the probability model of the scene in the video feed;
- selecting the first portion of the video feed and the second portion of the video feed based on the probability of the object being present in the first portion of the video feed compared to the probability of the object being present in the second portion of the video feed;
- wherein the probability of the object being present in the second portion of the video feed is greater than the probability of the object being present in the first portion of the video feed;
- processing a first portion of the video feed using a first detection algorithm to detect the object in the first portion of the video feed;
- and
- processing a second portion of the video feed using a second detection algorithm to detect the object in the second portion of the video feed,
- wherein the first detection algorithm is different from the second detection algorithm, and
- wherein the probability model includes a spatial probability model, that maps probabilities of detecting the object to regions within the scene, and a temporal probability model that maps probabilities of detecting the object to time periods.
A non-transient computer readable storage device storing instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising:
- receiving a video feed of a scene;
- obtaining a probability model of the scene in the video feed, wherein the probability model is based on historical detections of an object within the scene;
- determining a probability of the object being present in a first portion of the video feed and a probability of the object being present in a second portion of the video feed based on the probability model of the scene in the video feed;
- selecting the first portion of the video feed and the second portion of the video feed based on the probability of the object being present in the first portion of the video feed compared to the probability of the object being present in the second portion of the video feed;
- wherein the probability of the object being present in the second portion of the video feed is greater than the probability of the object being present in the first portion of the video feed;
- processing a first portion of the video feed using a first detection algorithm to detect the object in the first portion of the video feed;
- and
- processing a second portion of the video feed using a second detection algorithm to detect the object in the second portion of the video feed,
- wherein the first detection algorithm is different from the second detection algorithm, and
- wherein the probability model includes a spatial probability model, that maps probabilities of detecting the object to regions within the scene, and a temporal probability model that maps probabilities of detecting the object to time periods.
Dependencies are inferred from the claim text.