Abstract: The present application is applicable to the technical field of image processing, and provides a target object tracking method and an apparatus, and a terminal device. The target object tracking method includes: obtaining an image sequence including a target object, wherein the image sequence includes a plurality of image; analyzing the plurality of image, and determining a motion vector and a motion vector residual of the target object in a current image; and inputting the motion vector, the motion vector residual, and a known target region of the target object in a previous image into a first target detection network, and determining a predicted target region of the target object in the current image. The present application can reduce the calculation amount and thus improve the operation speed of target object tracking.

Abstract: A multi-target tracking method includes: obtaining a first image and a second image including N tracking targets from the same target video file, and obtaining N first tracking boxes of the first image that precedes a preset number of frames of the second image, the N first tracking boxes configured to frame the N tracking targets in the first image; superposing and synthesizing the first second images to obtain a target image; inputting the target image into an hourglass network model for feature extraction, to output a target feature map; inputting the target feature map into a prediction network to output a thermodynamic diagram, a width and height value set, a minor deviation value set, and a feature vector set; determining N second tracking boxes configured to frame N tracking targets in the second image, based on the thermodynamic diagram, the above three sets, and the N first tracking boxes.

Abstract: The present application is applicable to the technical field of image processing, and provides a target object tracking method and an apparatus, and a terminal device. The target object tracking method includes: obtaining an image sequence including a target object, wherein the image sequence includes a plurality of image; analyzing the plurality of image, and determining a motion vector and a motion vector residual of the target object in a current image; and inputting the motion vector, the motion vector residual , and a known target region of the target object in a previous image into a first target detection network, and determining a predicted target region of the target object in the current image. The present application can reduce the calculation amount and thus improve the operation speed of target object tracking.

Abstract: A multi-target tracking method includes: obtaining a first image and a second image including N tracking targets from the same target video file, and obtaining N first tracking boxes of the first image that precedes a preset number of frames of the second image, the N first tracking boxes configured to frame the N tracking targets in the first image; superposing and synthesizing the first second images to obtain a target image; inputting the target image into an hourglass network model for feature extraction, to output a target feature map; inputting the target feature map into a prediction network to output a thermodynamic diagram, a width and height value set, a minor deviation value set, and a feature vector set; determining N second tracking boxes configured to frame N tracking targets in the second image, based on the thermodynamic diagram, the above three sets, and the N first tracking boxes.

Abstract: A data receiving method is disclosed. The method includes: A wireless local area network (WLAN) receiving device receives, by using a first frequency module through a first channel, first data that is sent by a WLAN sending device. The WLAN receiving device receives, by using a second radio frequency module through a second channel, second data that is sent by the WLAN sending device. The WLAN receiving device obtains a received information field based on an information field of the first data and/or an information field of the second data. The WLAN receiving device obtains a received check field based on a check field of the first data and/or a check field of the second data. The WLAN receiving device attempts to check the received information field by using the received check field to obtain an original information field.

Abstract: An automatic test device and method for auditory brainstem response (ABR) collects an ABR dataset at a plurality of sound loudness levels, increases the times of level averaging by iteration based on an adaptive average method, and improves a signal-to-noise ratio until ABR signal detection conditions are met. Signal detection includes determining that the time lag between average curves obtained from the ABR dataset is within a specified range. Iteration is terminated when the ABR signal is detected or a maximum number of iterations is reached. A minimum loudness level required to detect the ABR signal is used as a hearing threshold. An accurate loudness level corresponding to the hearing threshold is obtained by function fitting on the number of iterations used at each loudness level and interpolation. The threshold detection can effectively reduce the number of times that an ABR recording needs to be acquired.

Abstract: An improved system and method for forming falling hot glass gobs which will be molded into glass containers in an Individual Section (IS) machine is disclosed which controls the weight, length, and shape of such glass gobs as they are produced. The system includes a gob feeder apparatus and a gob shearing mechanism a controller using models being used to correlate the effect of a number of feeder control settings on gob weight, length, and, shape. The controller is configured to adjust various feeder control settings based on a prioritized order of the feeder control settings such that gobs having different physical characteristics for different ones of the individual sections can be produced during a single machine cycle. By implementing the models, a closed loop system has been developed to set up controls to achieve the desired gob forming for each individual section and cavity.

Abstract: An improved system and method for forming falling hot glass gobs which will be molded into glass containers in an Individual Section (IS) machine is disclosed which controls the weight, length, and shape of such glass gobs as they are produced. The system includes a gob feeder apparatus and a gob shearing mechanism a controller using models being used to correlate the effect of a number of feeder control settings on gob weight, length, and, shape. The controller is configured to adjust various feeder control settings based on a prioritized order of the feeder control settings such that gobs having different physical characteristics for different ones of the individual sections can be produced during a single machine cycle. By implementing the models, a closed loop system has been developed to set up controls to achieve the desired gob forming for each individual section and cavity.

Abstract: An optimized scoop for receiving the glass gobs formed by the shearing mechanism is disclosed which provides an optimal trajectory that enables glass gobs passing therethrough to have an improved glass gob shape together with a negligible increase in glass gob length, with a velocity that is equal to or better than that of previously known scoops. The optimized scoop enhances glass gob shape to produce a more uniformly cylindrical glass gobs and eliminate dog-bone configurations. Trajectory of the optimized scoop is optimized both to enhance exit velocity of the glass gobs and minimize forces applied to the glass gobs.