Realtek Semiconductor Inc.

Chao-Hsun Yang

Shih-Tse Chen

Chien-Hao Chen

Shang-Lun Chan

Chun-Chang Wu

Chih-Wei Wu

Tsung-Hsuan Li

A method of data augmentation is provided. The method includes the following operations: selecting an original image from an original dataset including label data configured to indicate a labeled area of the original image; selecting at least part of content, located in the labeled area, of the original image as a first target image; generating a first sample image according to the first target image, in which the first sample image includes the first target image and a first border pattern different from the first target image, and the content, located in the labeled area, of the original image is free from including at least part of the first border pattern; and incorporating the first sample image into a sample dataset, in which the sample dataset is configured to be inputted to a machine learning model.

Method of data augmentation and non-transitory computer readable media

A method used for object tracking includes: using a specific object model to generate a first vector of a first ratio object and a second vector of a second ratio object of an image in an object detection bounding box of a specific frame; generating an identity label of an object within the bounding box according to the first vector, the second vector, and M first ratio reference vectors and M second ratio reference vectors stored in an object vector database.

Electronic apparatus and method used for object tracking

A method of performing person re-identification includes: obtaining a person feature vector according to an extracted image containing a person; obtaining state information of the person according to a state of the person in the extracted image; comparing the person feature vector with a plurality of registered person feature vectors in a database; when the person feature vector successfully matches a first registered person feature vector of the plurality of registered person feature vectors, identifying the person as a first identity corresponding to the first registered person feature vector; and selectively utilizing the person feature vector to update one of the first registered person feature vector and at least one second registered person feature vector that correspond to the first identity according to the state information.

Method and apparatus for person re-identification

A character recognition method includes the following operations: determining that the image of character to be identified corresponds to a matching character of several registered characters according to several vector distances to be identified between a vector of an image of character to be identified and several vectors of several registered character images of several registered characters, and storing a matching vector distance between the vector of the image of character to be identified and a vector of the matching character by a processor; and storing a data of the matching character according to the image of character to be identified when the matching vector distance is less than a vector distance threshold by the processor.

Character recognition method, character recognition device and non-transitory computer readable medium

The present invention provides a processing circuit applied to a face recognition system, which includes a characteristic value calculation module, a determination circuit and a threshold value calculation module. The characteristic value calculation module is used to receive an image and process the image to generate a specific characteristic value; when the face recognition system operates in a face recognition phase, the determination circuit calculates multiple differences each between the specific characteristic value and one of multiple reference characteristic values, and determines whether at least one of the multiple differences is lower than a threshold value to generate a determination result; and when the face recognition system operates in a face registration phase, the threshold value calculation module determines a new threshold value according to differences between the specific characteristic value and the multiple reference values, for updating the threshold value used by the determination circuit.

Processing circuit and processing method applied to face recognition system

An object detection method and an associated electronic device are provided, wherein the object detection method includes: utilizing an image processing circuit to determine whether motion occurs in an image to generate a determination result; selectively utilizing a specific bounding box to identify a target object to generate an identification result according to the determination result, wherein the specific bounding box represents a location of the target object in a previous image; and selectively updating information of the specific bounding box according to the identification result.

Object detection method for static scene and associated electronic device

A motion image integration method includes acquiring a raw image, detecting a first motion region image and a second motion region image by using a motion detector according to the raw image, merging the first motion region image with the second motion region image for generating a motion object image according to a relative position between the first motion region image and the second motion region image, and cropping the raw image to generate a sub-image corresponding to the motion object image according to the motion object image. A range of the motion object image is greater than or equal to a total range of the first motion region image and the second motion region image. Shapes of the first motion region image, the second motion region image, and the motion object image are polygonal shapes.

Motion image integration method and motion image integration system capable of merging motion object images

A motion detection method includes acquiring a raw image, detecting a motion object image according to the raw image by using a motion detector, cropping the raw image to generate a sub-image according to the motion object image, and inputting the sub-image to a processor for determining if a motion object of the sub-image matches with a detection category. The processor includes a neural network. The shape of the sub-image is a polygonal shape.

Motion detection method and motion detection system with low computational complexity and high detection accuracy

A method of updating background model for an image processing device is disclosed. The method includes receiving at least a background image, counting a number of occurrences of a color of each pixel of the background image according to a color information of each pixel of the at least a background image, to establish a background model including a statistical color information about the color and the number of occurrences of the color, receiving a current image, and determining whether to update the number of occurrences of the color in the background model corresponding to a color of each pixel of the current image according to a color information of each pixel of the current image and a result of comparing a random number with a threshold.

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Taipei, Taiwan

Chao-Hsun Yang