Abstract: A synchronous control system is provided. A transmission device 1 includes a reception unit 11 that receives a control signal for a lighting device in a live event site and a transmission unit 13 that transmits, to a reception device in a live viewing site, the control signal to which an output time is appended. The output time is obtained by adding a predetermined period of time to a receipt time at which the control signal is received. A reception device 2 includes a reception unit 21 that receives the control signal to which the output time is appended, and an output control unit 13 that outputs the control signal to a lighting device 6 in the live viewing site at the output time, and allows the control of the lighting device 6 to synchronize with at least one of video and audio to be played in the live viewing site.

Abstract: Accurate and rapid identification can be performed even when feature vectors of input pixels and background pixels are close.

Abstract: To identify and track a plurality of objects. An object tracking device 1 includes a frame acquisition unit 31 for acquiring one set of frame data 11 included in moving image data that captures a space where a plurality of objects are present; a distance acquisition unit 32 for acquiring measurement data 12 from measurement of three-dimensional coordinates of points configuring the objects in the space where the objects are present; an object position detection unit 33 for detecting a two-dimensional position of each of the objects from the frame data 11 on the basis of comparison with template data of each of the objects; an object distance detection unit 34 for detecting a plurality of three-dimensional positions of the objects from the measurement data 12; and an associating unit 35 for associating each two-dimensional position from the object position detection unit 33 with the nearest three-dimensional position among the three-dimensional positions from the object distance detection unit 34.

Abstract: A video generation device includes: an object extraction unit 11 that extracts an object area from a captured video in a space; a spatial position tracking unit 12 that detects an article from three-dimensional position measurement data in the space, applies identification information to the article, and calculates a three-dimensional spatial position of the article using the three-dimensional position measurement data; a position information merging unit 13 that links the object area to the identification information of the article to associate the three-dimensional spatial position with the object area; a depth expression unit 14 that generates a depth expression video of only the object area with which the three-dimensional spatial position has been associated, a depth of the video being able to be adjusted in the depth expression video; and a feedback unit 15 that transmits information indicating a method for reducing blurring generated in the object area of the depth expression video to any one or more o

Abstract: A selective PEG algorithm, creating a sparse matrix while maintaining row weight/column weight at arbitrary multi-levels, and in the process, inactivating an arbitrary edge so that a minimum loop formed between arbitrary nodes is enlarged or performing constrained interleaving, so that encoding efficiency in the case where a matrix space is narrow is improved.

Abstract: There is provided an object tracking apparatus that realizes robust object detection and tracking even for movement fluctuation and observation noise, an object tracking method and a computer program. An object cracking apparatus 1 is an apparatus tracking an object in video, the object tracking apparatus 1 including: a deep learning discriminator 2 which is a discriminator by deep learning; and a particle filter function unit 3 tracking an object by applying a multi-channel feature value of video including feature values by the deep learning discriminator 2 to likelihood evaluation by a particle filter, according to a distance between position information about the multi-channel feature value and position information about each particle.

Abstract: To identify and track a plurality of objects. An object tracking device 1 includes a frame acquisition unit 31 for acquiring one set of frame data 11 included in moving image data that captures a space where a plurality of objects are present; a distance acquisition unit 32 for acquiring measurement data 12 from measurement of three-dimensional coordinates of points configuring the objects in the space where the objects are present; an object position detection unit 33 for detecting a two-dimensional position of each of the objects from the frame data 11 on the basis of comparison with template data of each of the objects; an object distance detection unit 34 for detecting a plurality of three-dimensional positions of the objects from the measurement data 12; and an associating unit 35 for associating each two-dimensional position from the object position detection unit 33 with the nearest three-dimensional position among the three-dimensional positions from the object distance detection unit 34.

Abstract: Accurate and rapid identification can be performed even when feature vectors of input pixels and background pixels are close.

Abstract: There is provided an object tracking apparatus that realizes robust object detection and tracking even for movement fluctuation and observation noise, an object tracking method and a computer program. An object cracking apparatus 1 is an apparatus tracking an object in video, the object tracking apparatus 1 including: a deep learning discriminator 2 which is a discriminator by deep learning; and a particle filter function unit 3 tracking an object by applying a multi-channel feature value of video including feature values by the deep learning discriminator 2 to likelihood evaluation by a particle filter, according to a distance between position information about the multi-channel feature value and position information about each particle.

Abstract: This method and device makes it possible to implement maximum likelihood decoding of a sparse graph code at low computational complexity in the maximum likelihood decoding of the sparse graph code. This is, in the maximum likelihood of decoding of the sparse graph code, a lost data decoding process by a trivial decoding method and a lost data decoding process by a Gauss elimination method are performed repeatedly and alternately.

Abstract: A selective PEG algorithm, creating a sparse matrix while maintaining row weight/column weight at arbitrary multi-levels, and in the process, inactivating an arbitrary edge so that a minimum loop formed between arbitrary nodes is enlarged or performing constrained interleaving, so that encoding efficiency in the case where a matrix space is narrow is improved.

Abstract: This method and device makes it possible to implement maximum likelihood decoding of a sparse graph code at low computational complexity in the maximum likelihood decoding of the sparse graph code. This is, in the maximum likelihood of decoding of the sparse graph code, a lost data decoding process by a trivial decoding method and a lost data decoding process by a Gauss elimination method are performed repeatedly and alternately.