According to an embodiment, an image output device includes an extractor, a search unit, an associate unit, and a controller. The extractor is configured to extract a first parameter that varies in accordance with a movement of an object from at least one first image of the object, and extract a second parameter that varies in accordance with a movement of the object from each second image of the object. The search unit is configured to search for a second parameter similar to the first parameter. The associate unit is configured to associate the first image from which the first parameter is extracted with the second image from which the second parameter that is retrieved with respect to the first parameter is extracted. The controller is configured to instruct an output unit to output an image based on the first and second images that are associated to each other.

A method and apparatus are provided for optimizing one or more object detection parameters used by an autonomous vehicle to detect objects in images. The autonomous vehicle may capture the images using one or more sensors. The autonomous vehicle may then determine object labels and their corresponding object label parameters for the detected objects. The captured images and the object label parameters may be communicated to an object identification server. The object identification server may request that one or more reviewers identify objects in the captured images. The object identification server may then compare the identification of objects by reviewers with the identification of objects by the autonomous vehicle. Depending on the results of the comparison, the object identification server may recommend or perform the optimization of one or more of the object detection parameters.

A method and apparatus are provided for optimizing one or more object detection parameters used by an autonomous vehicle to detect objects in images. The autonomous vehicle may capture the images using one or more sensors. The autonomous vehicle may then determine object labels and their corresponding object label parameters for the detected objects. The captured images and the object label parameters may be communicated to an object identification server. The object identification server may request that one or more reviewers identify objects in the captured images. The object identification server may then compare the identification of objects by reviewers with the identification of objects by the autonomous vehicle. Depending on the results of the comparison, the object identification server may recommend or perform the optimization of one or more of the object detection parameters.

There are disclosed various methods and apparatuses for calibration of a multi-camera system. In some embodiments of the method a first image captured by a first camera unit of a multi-camera system and a second image captured by a second camera unit of the multi-camera system are obtained. A two-dimensional optical flow between the first camera unit and the second camera unit is determined by using the first image and the second image. The two-dimensional optical flow is converted into a three-dimensional rotation. A parallax component of the three-dimensional rotation is removed by using extrinsic parameters of the first camera unit and the second camera unit. The modified three-dimensional rotations are used to obtain a first error estimate for the first camera unit and a second error estimate for the second camera unit. In some embodiments the apparatus comprises means for implementing the method.

There are disclosed various methods and apparatuses for calibration of a multi-camera system. In some embodiments of the method a first image captured by a first camera unit of a multi-camera system and a second image captured by a second camera unit of the multi-camera system are obtained. A two-dimensional optical flow between the first camera unit and the second camera unit is determined by using the first image and the second image. The two-dimensional optical flow is converted into a three-dimensional rotation. A parallax component of the three-dimensional rotation is removed by using extrinsic parameters of the first camera unit and the second camera unit. The modified three-dimensional rotations are used to obtain a first error estimate for the first camera unit and a second error estimate for the second camera unit. In some embodiments the apparatus comprises means for implementing the method.

A moving object detection method and a moving object detection system are provided. The method includes: predetermining a background image corresponding to a scene monitored by a video monitoring device; performing a subtraction processing on a grayscale image to be detected and the background image to acquire a difference image; and binarizing the difference image and determining a moving object in the grayscale image to be detected, where the determining the background image includes: dividing a first grayscale image frame and a second grayscale image frame in a grayscale image frame sequence captured by the video monitoring device into image blocks to acquire a first image block set and a second image block set respectively, and determining the background image using a difference between the first image block set and the second image block set.

A moving object detection method and a moving object detection system are provided. The method includes: predetermining a background image corresponding to a scene monitored by a video monitoring device; performing a subtraction processing on a grayscale image to be detected and the background image to acquire a difference image; and binarizing the difference image and determining a moving object in the grayscale image to be detected, where the determining the background image includes: dividing a first grayscale image frame and a second grayscale image frame in a grayscale image frame sequence captured by the video monitoring device into image blocks to acquire a first image block set and a second image block set respectively, and determining the background image using a difference between the first image block set and the second image block set.

A motor vehicle vision system (10) includes a pair of imaging devices (12a, 12b) forming a stereo imaging apparatus (11) and a data processing apparatus (14) for rectification of images captured by the stereo imaging apparatus (11), matching of rectified images, and to detect an object in the surrounding of the motor vehicle. The data processing device (14) performs, for image elements (43) of a rectified image from one imaging device, a search for a best-matching image element (44) in the corresponding rectified image from the other imaging device. The search yielding vertical shift information from which a vertical shift from the image element (43) to the best-matching image element (44) is derivable. The data processing device (14) calculates a pitch angle error and/or a roll angle error of or between the imaging devices (12a, 12b) from the vertical shift information.

A motor vehicle vision system (10) includes a pair of imaging devices (12a, 12b) forming a stereo imaging apparatus (11) and a data processing apparatus (14) for rectification of images captured by the stereo imaging apparatus (11), matching of rectified images, and to detect an object in the surrounding of the motor vehicle. The data processing device (14) performs, for image elements (43) of a rectified image from one imaging device, a search for a best-matching image element (44) in the corresponding rectified image from the other imaging device. The search yielding vertical shift information from which a vertical shift from the image element (43) to the best-matching image element (44) is derivable. The data processing device (14) calculates a pitch angle error and/or a roll angle error of or between the imaging devices (12a, 12b) from the vertical shift information.

A system is provided for determining coordinated motion between objects. The system includes a velocity data receiving component, a position data receiving component, a multidimensional indexing component and a determining component. The velocity data receiving component receives velocity data of the objects. The position data receiving component receives position data of the objects. The multidimensional indexing component generates multidimensional indices of the objects based on the velocity data and position data. The determining component determines whether there is coordinated motion between objects based on the multidimensional indices.