Systems, methods, and computer-readable for multi-spatial scale object detection include generating one or more object trackers for tracking at least one object detected from on one or more images. One or more blobs are generated for the at least one object based on tracking motion associated with the at least one object. One or more tracklets are generated for the at least one object based on associating the one or more object trackers and the one or more blobs, the one or more tracklets including one or more scales of object tracking data for the at least one object. One or more uncertainty metrics are generated using the one or more object trackers and an embedding of the one or more tracklets. A training module for detecting and tracking the at least one object using the embedding and the one or more uncertainty metrics is generated using deep learning techniques.

Systems and methods for managing Application Programming Interfaces (APIs) are disclosed. Systems may involve automatically generating a honeypot. For example, the system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving, from a client device, a call to an API node and classifying the call as unauthorized. The operation may include sending the call to a node-imitating model associated with the API node and receiving, from the node-imitating model, synthetic node output data. The operations may include sending a notification based on the synthetic node output data to the client device.

Systems and methods for managing Application Programming Interfaces (APIs) are disclosed. Systems may involve automatically generating a honeypot. For example, the system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving, from a client device, a call to an API node and classifying the call as unauthorized. The operation may include sending the call to a node-imitating model associated with the API node and receiving, from the node-imitating model, synthetic node output data. The operations may include sending a notification based on the synthetic node output data to the client device.

A method for controlling a robotic device is presented. The method includes positioning the robotic device within a task environment. The method also includes mapping descriptors of a task image of a scene in the task environment to a teaching image of a teaching environment. The method further includes defining a relative transform between the task image and the teaching image based on the mapping. Furthermore, the method includes updating parameters of a set of parameterized behaviors based on the relative transform to perform a task corresponding to the teaching image.

A system and method for large-scale lane marking detection using multimodal sensor data are disclosed. A particular embodiment includes: receiving image data from an image generating device mounted on a vehicle; receiving point cloud data from a distance and intensity measuring device mounted on the vehicle; fusing the image data and the point cloud data to produce a set of lane marking points in three-dimensional (3D) space that correlate to the image data and the point cloud data; and generating a lane marking map from the set of lane marking points.

A system and method for large-scale lane marking detection using multimodal sensor data are disclosed. A particular embodiment includes: receiving image data from an image generating device mounted on a vehicle; receiving point cloud data from a distance and intensity measuring device mounted on the vehicle; fusing the image data and the point cloud data to produce a set of lane marking points in three-dimensional (3D) space that correlate to the image data and the point cloud data; and generating a lane marking map from the set of lane marking points.

Provided are an apparatus and method for sensing a moving ball, which extract a feature portion such as a trademark, a logo, etc. indicated on a ball from consecutive images of a moving ball, acquired by an image acquisition unit embodied by a predetermined camera device, and calculate a spin axis and spin amount of rotation the moving ball based on the feature portion and thus spin of the ball is simply, rapidly, and accurately calculated with low computational load, thereby achieving rapid and stable calculation of the ball in a relatively low performance system. The sensing apparatus includes an image acquisition unit for acquiring consecutive images, an image processing unit for extracting a feature portion from the acquired image, and a spin calculation unit for calculating spin using the extracted feature portion.

Provided are an apparatus and method for sensing a moving ball, which extract a feature portion such as a trademark, a logo, etc. indicated on a ball from consecutive images of a moving ball, acquired by an image acquisition unit embodied by a predetermined camera device, and calculate a spin axis and spin amount of rotation the moving ball based on the feature portion and thus spin of the ball is simply, rapidly, and accurately calculated with low computational load, thereby achieving rapid and stable calculation of the ball in a relatively low performance system. The sensing apparatus includes an image acquisition unit for acquiring consecutive images, an image processing unit for extracting a feature portion from the acquired image, and a spin calculation unit for calculating spin using the extracted feature portion.

There is provided a method of motion tracking comprising arranging one or more active marker devices on an object, the active marker devices being configured to emit light and each having an associated temporally repeating pattern comprising a plurality of time frames, controlling the one or more active marker devices to emit light according to their respective temporally repeating patterns, wherein the temporally repeating patterns are such that the active marker device does not emit light during at least one time frame of the plurality of time frames, detecting light emitted by the one or more active marker devices using one or more cameras, and determining a spatial configuration of the object using the light detected by the one or more cameras.

The present application provides an action recognition method and apparatus and an electronic device. The method includes: if a target object is detected from a video frame, acquiring a plurality of images containing the target object, and optical-flow images of the plurality of images; extracting an object trajectory feature of the target object from the plurality of images, and extracting an optical-flow trajectory feature of the target object from the optical-flow images of the plurality of images; and according to the object trajectory feature and the optical-flow trajectory feature, recognizing a type of an action of the target object. Because it combines the time-feature information and the spatial-feature information of the target object, effectively increases the accuracy of the detection and recognition on the action type, and may take into consideration the detection efficiency at the same time, thereby improving the overall detection performance.