A computer-implemented method for tracking includes obtaining an infrared image and a visible image from an imaging device supported by a carrier of an unmanned aerial vehicle (UAV), obtaining a combined image based on the infrared image and the visible image, identifying a target in the combined image, and generating control signals for tracking the identified target using the imaging device.

The measurement system 100 includes: a measurement apparatus 20 that measures vibrations of an object 40; an imaging apparatus 30 that is located so as to capture an image of the measurement apparatus 20; and a correction processing apparatus 10. the correction processing apparatus 10 includes: a displacement calculation unit 11 that calculates a displacement of the measurement apparatus 20 based on time-series images of the measurement apparatus 20 output from the imaging apparatus 30; a movement amount calculation unit 12 that calculates an amount of movement of the measurement apparatus 20 relative to the imaging apparatus 30, based on the displacement; and a correction processing unit 13 that corrects vibrations of the object measured by the measurement apparatus 20, using the calculated amount of movement of the measurement apparatus 20.

An electronic home plate system includes a home plate enclosure and at least one image sensor system disposed within the enclosure, each image sensor system including an image sensor, a lens and a first processor. The first processor is adapted to detect a motion of a ball by continuously capturing frames from the image sensor. The detected motion includes the ball passing over the home plate enclosure or the ball passing near and not over the home plate enclosure.

An electronic home plate system includes a home plate enclosure and at least one image sensor system disposed within the enclosure, each image sensor system including an image sensor, a lens and a first processor. The first processor is adapted to detect a motion of a ball by continuously capturing frames from the image sensor. The detected motion includes the ball passing over the home plate enclosure or the ball passing near and not over the home plate enclosure.

A surveillance system is provided. The surveillance system is configured for (i) detecting and tracking persons locally for each camera input video stream using the common area anchor boxes and assigning each detected ones of the persons a local track id, (ii) associating a same person in overlapping camera views to a global track id, and collecting associated track boxes as the same person moves in different camera views over time using a priority queue and the local track id and the global track id, (iii) performing track data collection to derive a spatial transformation through matched track box spatial features of a same person over time for scene coverage and (iv) learning a multi-camera tracker given visual features from matched track boxes of distinct people across cameras based on the derived spatial transformation.

A surveillance system is provided. The surveillance system is configured for (i) detecting and tracking persons locally for each camera input video stream using the common area anchor boxes and assigning each detected ones of the persons a local track id, (ii) associating a same person in overlapping camera views to a global track id, and collecting associated track boxes as the same person moves in different camera views over time using a priority queue and the local track id and the global track id, (iii) performing track data collection to derive a spatial transformation through matched track box spatial features of a same person over time for scene coverage and (iv) learning a multi-camera tracker given visual features from matched track boxes of distinct people across cameras based on the derived spatial transformation.

In accordance with an embodiment, a method of detecting moving objects via a moving camera includes receiving a sequence of images from the moving camera; determining optical flow data from the sequence of images; decomposing the optical flow data into global motion related motion vectors and local object related motion vectors; calculating global motion parameters from the global motion related motion vectors; calculating moto-compensated vectors from the local object related motion vectors and the calculated global motion parameters; compensating the local object related motion vectors using the calculated global motion parameters; and clustering the compensated local object related motion vectors to generate a list of detected moving objects.

In accordance with an embodiment, a method of detecting moving objects via a moving camera includes receiving a sequence of images from the moving camera; determining optical flow data from the sequence of images; decomposing the optical flow data into global motion related motion vectors and local object related motion vectors; calculating global motion parameters from the global motion related motion vectors; calculating moto-compensated vectors from the local object related motion vectors and the calculated global motion parameters; compensating the local object related motion vectors using the calculated global motion parameters; and clustering the compensated local object related motion vectors to generate a list of detected moving objects.

Provided are an object detection system and an object detection method. An object detection system may include a feature map extraction module configured to receive an image for object detection and extract a feature map having multiple resolutions for the image; a bounding box detection module configured to classify a bounding box by applying a first group of convolutional layers to the feature map, and predict the bounding box by applying a second group of convolutional layers to the feature map; and a mask generation module configured to generate a mask for the shape of the object in the bounding box using the feature map.

A video coding device may be configured to perform directional Bi-directional optical flow (BDOF) refinement on a coding unit (CU). The device may determine the direction in which to perform directional BDOF refinement. The device may calculate the vertical direction gradient difference and the horizontal direction gradient difference for the CU. The vertical direction gradient difference may indicate the difference between the vertical gradients for a first reference picture and the vertical gradients for a second reference picture. The horizontal direction gradient difference may indicate the difference between the horizontal gradients for the first reference picture and the horizontal gradients for the second reference picture. The video coding device may determine the direction in which to perform directional BDOF refinement based on the vertical direction gradient difference and the horizontal direction gradient difference. The video coding device may perform directional BDOF refinement in the determined direction.