Abstract: Disclosed is a method and device for regulating imaging accuracy of a motion-sensing camera. The method comprises: acquiring an infrared speckle pattern in a target infrared scene; recognizing an actual definition, an actual speckle regularity, and an actual central region brightness of the infrared speckle pattern; comparing the actual definition with a preset definition, the actual speckle regularity with a preset speckle regularity, and the actual central region brightness with a preset brightness; and adjusting an imaging focal length according to a comparison result, and completing regulation of the imaging accuracy. In this way, the method for regulating imaging accuracy of a motion-sensing camera of the present invention can obtain an infrared speckle pattern having required imaging accuracy.

Abstract: Disclosed is a method and device for regulating imaging accuracy of a motion-sensing camera. The method comprises: acquiring an infrared speckle pattern in a target infrared scene; recognizing an actual definition, an actual speckle regularity, and an actual central region brightness of the infrared speckle pattern; comparing the actual definition with a preset definition, the actual speckle regularity with a preset speckle regularity, and the actual central region brightness with a preset brightness; and adjusting an imaging focal length according to a comparison result, and completing regulation of the imaging accuracy. In this way, the method for regulating imaging accuracy of a motion-sensing camera of the present invention can obtain an infrared speckle pattern having required imaging accuracy.

Abstract: The present invention relates to a method for implementing a human skeleton tracking system based on depth data, specifically applied to a human skeleton tracking system based on depth data which is composed of a data acquisition unit, a limb segmentation unit, a skeleton point unit, and a tracking display unit. The units are in a relationship of sequential invocation. The limb segmentation unit uses scene depth data obtained after processing by the data acquisition unit to perform limb segmentation. The skeleton point unit uses the result obtained after segmentation by the limb segmentation unit to compute specific positions of respective skeleton points of a limb. The tracking display unit uses the positions of the skeleton points computed by the skeleton point unit to establish a skeleton model of the human body and perform tracking display.

Abstract: The present invention includes means of using a customized coprocessor to replace a main processor to process or interact with a data processing unit in a complex scene and reducing clock frequencies of the main processor and related modules, thereby achieving low power consumption of the three-dimensional measurement chip. The present invention includes: step 1: determining a system structure of a three-dimensional measurement processing chip; step 2: classifying data processing units according to scene requirements; step 3: adding coprocessors on a system bus; step 4: designing a customized coprocessor or small data control unit; step 5: designing a semi-customized coprocessor or small data control unit; step 6: optimizing software codes and reducing a clock frequency; step 7: verifying the chip to confirm that the chip satisfies design requirements. The present invention is applicable to a plurality of integrated circuits or any internetwork system consisting of a plurality of integrated circuits.

Abstract: The present invention relates to a method for implementing a human skeleton tracking system based on depth data, specifically applied to a human skeleton tracking system based on depth data which is composed of a data acquisition unit, a limb segmentation unit, a skeleton point unit, and a tracking display unit. The units are in a relationship of sequential invocation. The limb segmentation unit uses scene depth data obtained after processing by the data acquisition unit to perform limb segmentation. The skeleton point unit uses the result obtained after segmentation by the limb segmentation unit to compute specific positions of respective skeleton points of a limb. The tracking display unit uses the positions of the skeleton points computed by the skeleton point unit to establish a skeleton model of the human body and perform tracking display.

Abstract: The present invention includes means of using a customized coprocessor to replace a main processor to process or interact with a data processing unit in a complex scene and reducing clock frequencies of the main processor and related modules, thereby achieving low power consumption of the three-dimensional measurement chip. The present invention includes: step 1: determining a system structure of a three-dimensional measurement processing chip; step 2: classifying data processing units according to scene requirements; step 3: adding coprocessors on a system bus; step 4: designing a customized coprocessor or small data control unit; step 5: designing a semi-customized coprocessor or small data control unit; step 6: optimizing software codes and reducing a clock frequency; step 7: verifying the chip to confirm that the chip satisfies design requirements. The present invention is applicable to a plurality of integrated circuits or any internetwork system consisting of a plurality of integrated circuits.

Abstract: The present invention relates to an image background modeling and foreground extraction method based on a depth image, characterized by comprising: step 1: acquiring a depth image representing a distance from objects to a camera; step 2: initiating a real-time depth background model; step 3: updating the real-time depth background model; step 4: acquiring a current depth image representing the distance from the objects to the camera; step 5: extracting a foreground image of the current depth image based on the real-time depth background model; step 6: outputting the foreground image and generating a real-time target masking image; and step 7: updating the real-time depth background model, where code block information of each pixel point in the real-time depth background model is updated according to the real-time target masking image.