Abstract: To provide a technique which makes it possible to suitably estimate an important region and a non-important region in an image, an information processing apparatus includes: an obtaining means for obtaining input data which includes at least one of image data and point cloud data; an estimating means for estimating levels of importance with respect to a respective plurality of regions which are included in a frame indicated by the input data; a replacing means for generating replaced data by replacing at least one of the plurality of regions, which are included in the input data, with alternative data in accordance with the levels of importance; an evaluating means for deriving an evaluation value by referring to the replaced data; and a training means for training the estimating means with reference to the evaluation value.

Abstract: In order to make it possible to cope with fluctuations of a communication bandwidth, the processing control system (100) includes a switching control means (110) for carrying out control of which processing section analyzes analysis target data, a first processing section or a second processing section and a buffer control means (111) for causing one of the first processing section and the second processing section that is not analyzing the analysis target data to buffer the analysis target data.

Abstract: A model learning device determines a first machine learning model so as to further increase a combined loss function obtained by combining: a first loss function indicating the level of change in the reliability of a second image feature in a feature region of a reconstructed image, from the reliability of a first image feature in a feature region of the original image; and a second loss function indicating the level of recognition error. In addition, the model learning device: determines, so as to further reduce the combined loss function, respective parameter sets for a second machine learning model used in the generation of compressed data, and a third machine learning model used in the generation of the reconstructed image from the compressed data; and determines a parameter set for the fourth machine learning model in common with that for the first machine learning model.

Abstract: A video analysis apparatus and the like with improved video analysis accuracy are provided. The video analysis apparatus includes a first video analysis unit and a second video analysis unit. The first video analysis unit is configured to distribute frames to the first video analysis unit or the second video analysis unit, detect a target object in frames which are distributed to be analyzed in the first video analysis unit, and acquire information on movement associated with the detected target object, and transmitting the information on the movement and a detection result in the first detection unit to the second video analysis unit. The second video analysis unit is configured to detect a target object in frames from the distribution unit, and adjust the detection result of the first detection unit based on a detection result in the second detection unit and the information on the movement.

Abstract: A video processing system includes an image quality control apparatus and a detection apparatus. The image quality control apparatus includes an image quality control unit configured to control image quality of each region of a video, and a transmission unit configured to transmit, to the detection apparatus, the video of which the image quality is controlled. The detection apparatus includes a detection unit configured to detect information regarding an object in the video transmitted from transmission unit, and a notification unit configured to notify the image quality control apparatus of a detection result of the detection unit. The image quality control apparatus further includes a determination unit configured to determine the image quality of each region of the video according to the detection result notified from the notification unit, the image quality being controlled by the image quality control unit.

Abstract: A video processing system (10) includes an object detection unit (11) that detects an object included in a video input to the video processing system (10) in a case where the video is input to the video processing system (10). The video processing system (10) further includes a video quality control unit (12) that controls a video quality of a region including the object in the input video according to a situation related to the object detected from the input video in a case where the object detection unit (11) detects the object from the input video.

Abstract: A video processing apparatus according to one aspect of the present example embodiment includes: at least one memory configured to store instructions; and at least one processor configured to execute the instructions to: generate image quality feature information indicating a feature of image quality information indicating an image quality of a video in time and space; generate integrated data obtained by integrating information regarding a video including a feature of the video in time and space and the image quality feature information; and execute recognition processing on a subject included in the video based on the integrated data.

Abstract: The present disclosure provides an information processing apparatus and the like capable of specifying an object in an image that may affect traveling of a vehicle. An information processing apparatus includes: an acquisition unit that acquires an image captured by an image capturing unit mounted on a vehicle; an object detection unit that detects one or more objects in the acquired image; a traveling region specifying unit that specifies a traveling region, in which the vehicle is traveling, from regions in the acquired image; and a determination unit that determines, based on the traveling region, an image processing region, which is subjected to image processing, among regions of the one or more objects.

Abstract: A region prediction unit predicts the target region in the second image frame based on the target region detected in a first image frame and a time interval between the first and second image frames. The region prediction unit predicts the target region in the third image frame based on the target region detected in the second image frame and a time interval between the second and third image frames. A region determination unit determines, as an image-processing region, a region obtained by adding a margin to the target prediction region in the third image frame in accordance with an overlapping portion between the detected target region in the second image frame and the prediction region in the second image frame.

Abstract: Estimating determines, on the basis of processed quantized values, a quantization interval for quantizing input element values, which are elements of input values serving as a processing object and determines, on the basis of the processed quantized values, a probability distribution of quantization value candidates, which are candidates of quantized values of the input element values; quantizing uses the quantization interval to quantize the input element values and determine a quantized value; and entropy encoding uses the probability distribution to perform entropy encoding of a quantization time sequence which includes quantized value codes indicating the quantized values of the input element values.

Abstract: A video processing system (10) according to the present disclosure includes a recognition model (M1), a recognition model (M2), a recognition model (M3), and a recognition model (M4) that have learned video learning data corresponding to different video quality parameters, for each of the video quality parameters; and a selection unit (11) that selects a recognition model that performs recognition regarding a target included in the video input data to be input, from among the recognition model (M1), the recognition model (M2), the recognition model (M3), and the recognition model (M4), according to a video quality parameter of the video input data.

Abstract: Provided is a video processing system including: a detection means for detecting a tracking target from an input video; a trajectory prediction means for predicting a trajectory of the tracking target in the video; and a tracking means for tracking the tracking target, and when there is a missing region in the video, for estimating a position of the tracking target in the missing region using a prediction result of the trajectory prediction means. The video processing system may further include a determination means for determining whether or not the tracking target tracked by the tracking means is the same as the tracking target detected by the detection means; and a trajectory information output means for assigning identification information to the detected tracking target.

Abstract: A transmission method, a transmission system, and a system control device that are capable of transmitting a high-quality video suitable for remote operation while suppressing loss and delay are provided. The transmission method is a transmission method of transmitting an image photographed by a plurality of cameras mounted on a vehicle, via a network, and includes a bandwidth estimation step of estimating an available bandwidth of the network, and a camera bandwidth allocation step of allocating a bandwidth to each camera according to the available bandwidth and an importance degree of each camera.

Abstract: An object is to provide a video processing system, a video processing apparatus, and a video processing method that can be expected to improve recognition accuracy of an object in a video. A video processing system includes video acquisition means, time difference information acquisition mean, and recognition means. The video acquisition means acquires an input video. The time difference information acquisition means acquires first time difference information between the frames of the input video. The recognition means inputs the input video and the first time difference information between the frames of the input video to a trained recognition model trained using a training video and second time difference information between frames of the training video and recognizing an object in the input video.

Abstract: A video processing system (10) includes a recognition model (M1) that analyzes a video corresponding to a first video recognition environment; a recognition model (M2) that analyzes a video corresponding to a second video recognition environment; and a switching unit (11) that switches a recognition model for analyzing video input data to be input, from the recognition model (M1) to the recognition model (M2) according to a change from the first video recognition environment to the second video recognition environment in the video input data, in which the switching unit (11) inputs video input data including data preceding a switching timing by a predetermined period to the recognition model 10 (M2) according to the change from the first video recognition environment to the second video recognition environment in the video input data.

Abstract: A video processing system (10) includes an image quality control unit (11) that controls an image quality of a gaze region including a gaze target in an input video, a recognition unit (12) that performs recognition processing of recognizing the gaze target on the video in which the image quality of the gaze region is controlled, a prediction unit (13) that predicts a position of the gaze target in a video subsequent to the video on which the recognition processing has been performed, based on extraction information extracted from the recognition processing, and a determination unit (14) that determines the gaze region for which the image quality control means (11) controls an image quality in the subsequent video, based on the predicted position of the gaze target.

Abstract: The present disclosure provides a data analysis apparatus including a time series analysis state memory unit configured to store a state of a time series data analysis; a time series analysis unit configured to analyze input data points belonging to time series data generated by a plurality of data sources and update the state stored the time series analysis state memory unit; and a filtering apparatus including: a data fetching unit configured to fetch data points from the data sources; a quality target memory unit; and a data point selection optimization unit configured to select data points based on a quality target stored in the quality target memory unit and data input from the time series analysis state memory unit.

Abstract: A parameter set of a first machine learning model is determined such that a first loss function becomes larger. The first loss function indicates a degree of variation from a conditional confidence of a first image feature conditional on a third image feature to a conditional confidence of a second image feature conditional on the third image feature. The third image feature is an image feature for recognition of a subject extracted from an original image. The first image feature is discriminated in a specific region of the original image by using a first machine learning model. A parameter set of each of the second machine learning model and the third machine learning model are determined such that a second loss function becomes smaller.

Abstract: An information processing device includes: an image acquisition unit configured to acquire an image captured by an imaging unit mounted on a vehicle; a target detection unit configured to detect a target region including a target in the acquired image; a classification identification unit configured to identify classification including a type of the detected target and a size of the detected target region; and a region determination unit configured to determine a region obtained by adding a margin corresponding to the identified classification to the target region as an image processing region.

Abstract: An estimation unit of a visual object tracking apparatus estimates a plurality of estimated bounding boxes and estimated object IDs respectively corresponding to the estimated bounding boxes based on a plurality of predicted bounding boxes and a plurality of detected bounding boxes. For example, the “detected bounding boxes” are “bounding boxes (bounded areas)” detected by a detector in each of a plurality of frames in a time series such as moving images. The “bounding box” is a frame surrounding an image of an object detected in a frame. For example, the “predicted bounding box” is a “bounding box” predicted by a predictor based on an estimated bounding box(es) estimated for one or a plurality of frames in the past.