Surveillance systems with a plurality of cameras and image processing method thereof are provided. Based on a plurality of images captured by the plurality of cameras, some of the images are translucentized with other images and the other images are stitched with each other according to the space geometrical relations of cameras. The benefit of the surveillance system is that monitoring the field surveilled by each camera is no longer necessary to watch each image separately.

A method for registering existing vector data associated with a first image of a location to a second image of the location is provided. Typically the images are separated by an increment of time. The method is implemented by at least one computing device including at least one processor in communication with a memory. The method includes receiving, by the at least one computing device, the existing vector data associated with the first image of the location, receiving, by the at least one computing device, a plurality of controls for registering the first image to the second image, applying, by the at least one computing device, the plurality of controls to the existing vector data to generate updated vector data, and storing, in the memory, the updated vector data associated with the second image.

A non-transitory computer readable medium storing instructions to cause one or more processors to acquire an image data sequence containing images of an object in a scene along time and track a pose of the object through an object pose tracking algorithm. The processor may further, acquire a first pose of the object in a first image of the image data sequence, the first pose being a result of tracking a pose of the object through an object pose tracking algorithm, verify the first pose, extract 2D features of the object from the first image when the first pose is verified, and store a training dataset containing the extracted 2D features and the corresponding verified first pose in the one or more memories or other one or more memories.

In one embodiment, a computer accessible storage medium stores a plurality of instructions which, when executed: group a set of reconstructed three dimensional (3D) points derived from image data into a plurality of groups based on one or more attributes of the 3D points; select one or more groups from the plurality of groups; and sample data from the selected groups, wherein the sampled data is input to a consensus estimator to generate a model that describes a 3D model of a scene captured by the image data. Other embodiments may bias sampling into a consensus estimator for any data set, based on relative quality of the data set.

A method may include clustering form elements into line objects and columns of a table of a structured representation by applying a trained multi-dimensional clustering model to spatial coordinates of the form elements, and assigning a table header line type to a table header line object of the line objects based on a spatial coordinate of the table header line object relative to a spatial coordinate of a topmost table data line object of the line objects, and a determination that a number of columns of the table header line object is within a threshold of a number of columns of the topmost table data line object. The topmost table data line object may be assigned a table data line type. The method may further include presenting the structured representation to a user.

A method for bump alert, the method may include receiving by a vehicle computerized system, at least one visual bump indicator that is visible before driving over at least one bump; obtaining sensed information regarding an environment of the vehicle; processing the sensed information, wherein the processing comprises searching a visual bump indicator of the at least one visual bump indicator; determining whether the vehicle approaches a bump; and generating the bump alert when determining that the vehicle approaches the bump.

A video content matching system includes a computing platform having a hardware processor and a memory storing a software code. When executed, the software code obtains a reference digital profile of a reference video segment, obtains a target digital profile of target video content, and compares the reference and target digital profiles to detect a candidate video segment of the target video content for matching to the reference video segment. The software code also frame aligns reference video frames of the reference video segment with corresponding candidate video frames of the candidate video segment to provide frame aligned video frame pairs, pixel aligns the frame aligned video frame pairs to produce frame and pixel aligned video frame pairs, and identifies, using the frame and pixel aligned video frame pairs, the candidate video segment as a matching video segment or a non-matching video segment for the reference video segment.

Provided is an information processing device capable of determining accuracy of data acquired using an LIDAR sensor.

An information processing device (10) according to the present disclosure includes: an image-capturing sensor (11); a learning unit (12) configured to learn, as learning data, learning image-capturing data and likelihood of a distance between points included in 3D point cloud data with correct answer in a region substantially the same region included in the learning image-capturing data, and to generate a learned model; and an estimation unit (13) configured to use the learned model to generate estimation data including likelihood of a distance between points included in estimation 3D point cloud data determined based on estimation image-capturing data, from the estimation image-capturing data acquired by the image-capturing sensor (11).

When searching for a registered image similar to a query image, an image processing apparatus estimates an area having a difference by comparing images determined to be similar if a plurality of similar images is found at the time of search of a similar image using an image feature amount in order to enhance search accuracy. Then, the image processing apparatus compares the query image with the plurality of images similar to the query image based on the difference area.

The present disclosure relates to a target tracking method, device, system, and a non-transitory computer-readable storage medium. The method includes: determining an area where a current target is located by performing target detection on a current frame image; extracting a current position information of the area where the current target is located; predicting a position information of an area where each historical target is located at a corresponding moment of the current frame image based on historical position information of an area where each historical target is located in one or more historical frame images; determining a historical target that is the same target as the current target based on a position difference between the current position information and the predicted position information, and tracking the current target.