Abstract: An apparatus includes a base having a platform configured for placement of one or more items on a surface of the platform, a frame coupled to the base, and a plurality of camera units coupled to the frame. Each camera unit is configured to capture images of at least a portion of the platform and the plurality of camera units includes a plurality of side-view camera units arranged above the platform along a horizontal plane, wherein each camera unit of the side-view camera units comprises two vertically stacked cameras and is configured to capture perspective images of at least a portion of the platform. An overhead-view camera unit is positioned over the platform, wherein the overhead-view camera unit comprises two cameras and is configured to capture top view images of the platform.

Abstract: A device for object tracking receives an image from a camera, where the image shows a set of points on a calibration board placed on a platform. The device determines a pixel location array that comprises pixel locations associated with the points in the image. The device determines, by applying a first homography to the pixel location array, a calculated location array identifying calculated physical location coordinates of the set of points in the global plane. The device determines that the difference between a reference location array and the calculated location array is more than a threshold value. In response, the device determines that the camera and/or the platform has moved from a respective initial location when the first homography was determined. The device determines a second homography by multiplying an inverse of the pixel location array by the reference location array and calibrates the camera using the second homography.

Abstract: In response to detecting a first triggering event corresponding to placement of a first item on a platform, a plurality of first images are captured of the first item and a plurality of cropped first images are generated based on the first images. A first item identifier associated with the first item is identified based on the cropped first images. In response to detecting a second triggering event corresponding to placement of a second item on the platform, a plurality of second images of the first item are captured and a plurality of cropped second images are generated from the second images. In response to determining that the cropped first images match with the cropped second images, the first item identifier is assigned to the first item depicted in the second images.

Abstract: A reference depth image of a platform is captured using a three-dimensional (3D) sensor. A plurality of secondary depth images are captured, wherein for each secondary depth image, a depth difference parameter is determined by comparing the secondary depth image and the reference depth image. In response to determining that the depth difference parameter has stayed constant at a value higher than zero for a duration of a first time interval, it is determined that an item has been placed on the platform.

Abstract: A tracking system includes a camera subsystem that includes cameras that capture vide of a space. Each camera is coupled with a camera client that determines local coordinates of people in the captured video. The camera clients generate frames that include color frames and depth frames labeled with an identifier number of the camera and their corresponding timestamps. The camera clients generate tracks that include metadata describing historical people detections, tracking identifications, timestamps, and the identifier number of the camera. The camera clients send the frames and tracks to cluster servers that maintain the frames and tracks such that they are retrievable using their corresponding labels. A camera server queries the cluster servers to receive the frames and tracks using their corresponding labels. The camera server determines the physical positions of people in the space based on the determined local coordinates.

Abstract: A tracking system includes a camera subsystem that includes cameras that capture vide of a space. Each camera is coupled with a camera client that determines local coordinates of people in the captured video. The camera clients generate frames that include color frames and depth frames labeled with an identifier number of the camera and their corresponding timestamps. The camera clients generate tracks that include metadata describing historical people detections, tracking identifications, timestamps, and the identifier number of the camera. The camera clients send the frames and tracks to cluster servers that maintain the frames and tracks such that they are retrievable using their corresponding labels. A camera server queries the cluster servers to receive the frames and tracks using their corresponding labels. The camera server determines the physical positions of people in the space based on the determined local coordinates.

Abstract: A tracking system includes a camera subsystem that includes cameras that capture vide of a space. Each camera is coupled with a camera client that determines local coordinates of people in the captured video. The camera clients generate frames that include color frames and depth frames labeled with an identifier number of the camera and their corresponding timestamps. The camera clients generate tracks that include metadata describing historical people detections, tracking identifications, timestamps, and the identifier number of the camera. The camera clients send the frames and tracks to cluster servers that maintain the frames and tracks such that they are retrievable using their corresponding labels. A camera server queries the cluster servers to receive the frames and tracks using their corresponding labels. The camera server determines the physical positions of people in the space based on the determined local coordinates.

Abstract: A tracking system includes a camera subsystem that includes cameras that capture vide of a space. Each camera is coupled with a camera client that determines local coordinates of people in the captured video. The camera clients generate frames that include color frames and depth frames labeled with an identifier number of the camera and their corresponding timestamps. The camera clients generate tracks that include metadata describing historical people detections, tracking identifications, timestamps, and the identifier number of the camera. The camera clients send the frames and tracks to cluster servers that maintain the frames and tracks such that they are retrievable using their corresponding labels. A camera server queries the cluster servers to receive the frames and tracks using their corresponding labels. The camera server determines the physical positions of people in the space based on the determined local coordinates.

Abstract: A tracking system includes a camera subsystem that includes cameras that capture vide of a space. Each camera is coupled with a camera client that determines local coordinates of people in the captured video. The camera clients generate frames that include color frames and depth frames labeled with an identifier number of the camera and their corresponding timestamps. The camera clients generate tracks that include metadata describing historical people detections, tracking identifications, timestamps, and the identifier number of the camera. The camera clients send the frames and tracks to cluster servers that maintain the frames and tracks such that they are retrievable using their corresponding labels. A camera server queries the cluster servers to receive the frames and tracks using their corresponding labels. The camera server determines the physical positions of people in the space based on the determined local coordinates.

Abstract: A tracking system includes a camera subsystem that includes cameras that capture vide of a space. Each camera is coupled with a camera client that determines local coordinates of people in the captured video. The camera clients generate frames that include color frames and depth frames labeled with an identifier number of the camera and their corresponding timestamps. The camera clients generate tracks that include metadata describing historical people detections, tracking identifications, timestamps, and the identifier number of the camera. The camera clients send the frames and tracks to cluster servers that maintain the frames and tracks such that they are retrievable using their corresponding labels. A camera server queries the cluster servers to receive the frames and tracks using their corresponding labels. The camera server determines the physical positions of people in the space based on the determined local coordinates.