Abstract: Methods and apparatuses for accurately determining a model, which is to be the basis of transfer learning, among a plurality of source models, are provided. According to an embodiment, an apparatus for determining a base model to be used for transfer learning to a target domain is provided. The apparatus comprises a memory which comprises one or more instructions and a processor which executes the instructions to construct a neural network model for measuring suitability of a plurality of pre-trained source models, measure the suitability of each of the source models by inputting data of the target domain to the neural network model, and determine the base model to be used for the transfer learning among the source models based on the suitability.

Abstract: A method for generating artificial neural network architecture includes selecting any one of a plurality of artificial neural network architectures as a backbone architecture, calculating weights of a plurality of candidate operation blocks applicable to each stage for each of one or more stages constituting the backbone architecture, replacing or removing at least one of the plurality of candidate operation blocks based on the calculated weight, and repeatedly performing the calculating of the weight and the replacing or removing the at least one candidate operation block by a preset number of times to configure a final candidate operation block set for each stage and select any one of the final candidate operation block as an operation block of a corresponding stage.

Abstract: Methods and apparatuses for accurately determining a model, which is to be the basis of transfer learning, among a plurality of source models, are provided. According to an embodiment, an apparatus for determining a base model to be used for transfer learning to a target domain is provided. The apparatus comprises a memory which comprises one or more instructions and a processor which executes the instructions to construct a neural network model for measuring suitability of a plurality of pre-trained source models, measure the suitability of each of the source models by inputting data of the target domain to the neural network model, and determine the base model to be used for the transfer learning among the source models based on the suitability.

Abstract: There is provided a method of masking an object of non-interest by a masking apparatus. The method comprises acquiring first video information of a region of interest, determining a first object of non-interest area as an area in which motion vectors are present in the first video information of the region of interest, removing spatial noise from the first object of non-interest area to acquire a second object of non-interest area including at least a part of the first object of non-interest area and generating a mask corresponding to the second object of non-interest area.

Abstract: A method includes acquiring, by a device, encoded image data corresponding to an original image. The method includes decoding, by the device, the encoded image data. The method included acquiring, by the device, a foreground extraction target frame and an encoding parameter associated with an encoding process of the original image based on decoding the encoded image data. The method includes extracting, by the device, a first candidate foreground associated with the foreground extraction target frame based on the encoding parameter. The method includes extracting, by the device, a second candidate foreground associated with the foreground extraction target frame based on a preset image processing algorithm. The method includes determining, by the device, a final foreground associated with the foreground extraction target frame based on the first candidate foreground and the second candidate foreground.

Abstract: Disclosed is a method for reducing noise. The method comprises decoding a bitstream encoded with a first codec, acquiring Quantization Parameter (QP) values determined in a process of encoding the bitstream with the first codec, detecting a frame with noise by using the QP values and inputting the frame with noise to a predefined noise filter.

Abstract: A method for selecting an image for object recognition is provided. The method comprises receiving an image bitstream; acquiring predetermined first codec metadata information among codec metadata information from the received image bitstream; calculating a first quality measurement value using the acquired first codec metadata information; calculating a quality score of the image by using the calculated first quality measurement value; and selecting a predetermined number of images based on the calculated quality score of the image.

Abstract: Disclosed is a method for reducing noise. The method comprises decoding a bitstream encoded with a first codec, acquiring Quantization Parameter (QP) values determined in a process of encoding the bitstream with the first codec, detecting a frame with noise by using the QP values and inputting the frame with noise to a predefined noise filter.

Abstract: An apparatus and method for tracking an object using a feature descriptor and an apparatus and method for removing a garbage feature are disclosed. A feature descriptor generation unit generates a plurality of features descriptors indicating information of a plurality of features extracted from an input image from which an object of interest is desired to be detected. A matching unit matches the feature descriptors with feature descriptors of a target object stored in advance, and determines the feature descriptors of the object of interest corresponding to the target object. A feature point removal unit removes feature descriptors that do not meet a geometric comparison condition from among the feature descriptors of the object of interest, and establishes final feature descriptors of the object of interest.

Abstract: According to an aspect of the present invention, there is provided a people counting apparatus including: a reception unit which receives a video of an area including an entrance captured by a video capture device; a line setting unit which sets an inline at the entrance and sets an outline such that a specific region is formed on a side of the inline; a detection unit which detects moving objects in the video using information differences between frames of the received video and detects human moving objects among the detected moving objects; a tracking unit which tracks the movement of each of the detected moving objects; and a counting unit which determines whether each of the moving objects passed the inline and the outline based on the tracked movement of each of the moving objects and counts the number of people based on the determination result.

Abstract: According to an aspect of the present invention, there is provided a people counting apparatus including: a reception unit which receives a video of an area including an entrance captured by a video capture device; a line setting unit which sets an inline at the entrance and sets an outline such that a specific region is formed on a side of the inline; a detection unit which detects moving objects in the video using information differences between frames of the received video and detects human moving objects among the detected moving objects; a tracking unit which tracks the movement of each of the detected moving objects; and a counting unit which determines whether each of the moving objects passed the inline and the outline based on the tracked movement of each of the moving objects and counts the number of people based on the determination result.

Abstract: An apparatus and method for tracking an object using a feature descriptor and an apparatus and method for removing a garbage feature are disclosed. A feature descriptor generation unit generates a plurality of features descriptors indicating information of a plurality of features extracted from an input image from which an object of interest is desired to be detected. A matching unit matches the feature descriptors with feature descriptors of a target object stored in advance, and determines the feature descriptors of the object of interest corresponding to the target object. A feature point removal unit removes feature descriptors that do not meet a geometric comparison condition from among the feature descriptors of the object of interest, and establishes final feature descriptors of the object of interest.