Systems and methods for intelligent video surveillance
AssigneeShanghai Truthvision Information Technology Co., Ltd.
InventorZhong Zhang
Life of this patent
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Abstract
A method may include obtaining a video collected by a visual sensor, the video including a plurality of frames and detecting one or more objects from the video in at least a portion of the plurality of frames. The method may also include determining a first detection result associated with the one or more objects with a trained self-learning model. The method may further include selecting a target moving object of interest from the one or more objects at least in part based on the first detection result. The trained self-learning model may be provided based on a plurality of training samples collected by the visual sensor.
Claims20
A system, comprising:
- a storage device storing a set of instructions;
- and
- at least one processor configured to communicate with the storage device, wherein when executing the set of instructions, the at least one processor is directed to cause the system to perform operations including:
- obtaining a video collected by a visual sensor, the video including a plurality of frames;
- detecting, in at least a portion of the plurality of frames, one or more objects from the video;
- determining, with a trained self-learning model, a first detection result associated with the one or more objects;
- determining, based on the at least a portion of the plurality of frames, one or more behavior features associated with each of the one or more objects;
- determining, based on the one or more behavior features associated with each of the one or more objects, a second detection result associated with each of the one or more objects;
- and
- determining, based on the first detection result and the second detection result, a tartlet moving object of interest from the one or more objects, wherein the trained self-learning model is provided based on a plurality of training samples collected by the visual sensor.
A method implemented on a computing device having at least one processor and at least one computer-readable storage medium for abnormal scene detection, the method comprising:
- obtaining a video collected by a visual sensor, the video including a plurality of frames;
- detecting, in at least a portion of the plurality of frames, one or more objects from the video;
- determining, with a trained self-learning model, a first detection result associated with the one or more objects;
- determining, based on the at least a portion of the plurality of frames, one or more behavior features associated with each of the one or more objects;
- determining, based on the one or more behavior features associated with each of the one or more objects, a second detection result associated with each of the one or more objects;
- and
- determining, based on the first detection result and the second detection result, a target moving object of interest from the one or more objects, wherein the trained self-learning model is provided based on a plurality of training samples collected by the visual sensor.
A non-transitory computer readable medium, comprising:
- instructions being executed by at least one processor, causing the at least one processor to implement a method, comprising:
- obtaining a video collected by a visual sensor, the video including a plurality of frames;
- detecting, in at least a portion of the plurality of frames, one or more objects from the video;
- determining, with a trained self-learning model, a first detection result associated with the one or more objects;
- determining, based on the at least a portion of the plurality of frames, one or more behavior features associated with each of the one or more objects;
- determining, based on the one or more behavior features associated with each of the one or more objects, a second detection result associated with each of the one or more objects;
- and
- determining, based on the first detection result and the second detection result, a target moving object of interest from the one or more objects, wherein the trained self-learning model is provided based on a plurality of training samples collected by the visual sensor.
Dependencies are inferred from the claim text.