An information processing device that includes a control unit configured to track an object in an image using images input in time series, using a tracking result obtained by performing tracking in units of a tracking region corresponding to a specific part of the object.

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.

There is provided an optical navigation device including an image sensor and a processing unit. The image sensor outputs successive image frames. The processing unit calculates a contamination level and a motion signal based on filtered image frames, and determines whether to update a fixed pattern noise (FPN) stored in a frame buffer according to a level of FPN subtraction, the calculated contamination level and the calculated motion signal to optimize the update of the fixed pattern noise.

A method and a device for multi-object tracking, and an electronic device are provided. The method includes: determining a hybrid-time position map of a current point cloud fragment; converting a tracked position map of a previous point cloud fragment into a temporary tracked position map of the current point cloud fragment; and averaging the hybrid-time position map and the temporary tracked position map of the current point cloud fragment, to generate a tracked position map of the current point cloud fragment. With the method and the device for multi-object tracking, and the electronic device, the hybrid-time position map and temporary tracked position map of the current point cloud fragment are averaged, so that not only the tracked position map of the current point cloud fragment is accurately generated, but also an object ID is inherited. Based on the object ID, the same object in different point cloud fragments are associated, so that multi-object tracking is implemented without an association step in the conventional solutions. It is unnecessary to set additional hyper-parameters, and strong versatility is achieved.

An image processing method and a device configured to implement the same are disclosed. The device comprises: a hybrid imaging device configured to obtain optical input; and a processing device in signal communication with the hybrid imaging device. The processing device comprises: a motion detection circuit that performs feature tracking based on a first component of an obtained optical input; a motion estimation circuit that performs motion compensation based on output of the motion detection unit; a frame reconstruction circuit that reconstructs image frame based on both the output of the motion estimation unit and a second component of the optical input; and an output unit that outputs image frame at a predetermined global frame rate.

Systems and methods for image-based perception. The methods comprise: obtaining, by a computing device, images captured by a plurality of cameras with overlapping fields of view; generating, by the computing device, spatial feature maps indicating locations of features in the images; defining, by the computing device, predicted cuboids at each location of an object in the images based on the spatial feature maps; and assigning, by the computing device, at least two cuboids of said predicted cuboids to a given object when predictions from images captured by separate cameras of the plurality of cameras should be associated with a same detected object.

Embodiments are directed to neural network processing for multi-object three-dimensional (3D) modeling. An embodiment of a computer-readable storage medium includes executable computer program instructions for obtaining data from multiple cameras, the data including multiple images, and generating a 3D model for 3D imaging based at least in part on the data from the cameras, wherein generating the 3D model includes one or more of performing processing with a first neural network to determine temporal direction based at least in part on motion of one or more objects identified in an image of the multiple images or performing processing with a second neural network to determine semantic content information for an image of the multiple images.

A method of processing a sequence of images of a retina acquired by an ophthalmic device to generate retinal position tracking information indicative of retina movement during acquisition. The method includes (i) receiving one or more images of the retina; (ii) calculating a cross-correlation between a reference image and an image based on the received image(s) to acquire an offset between the image and reference image; and repeating processes (i) and (ii) to acquire, as the tracking information, respective offsets for images that are based on the respective received image(s). Another step includes modifying the reference image during the repeating, by determining a measure of similarity between correspondingly located regions of pixels in two or more received images and accentuating features in the reference image representing structures of the imaged retina in relation to other features in the reference image based on the determined measure of similarity.

Systems and methods for movement detection are provided. In one example embodiment, a computer-implemented method includes obtaining image data and range data representing a scene external to an autonomous vehicle, the image data including at least a first image and a second image that depict the scene. The method includes identifying a set of corresponding image features from the image data, the set of corresponding image features including a first feature in the first image having a correspondence with a second feature in the second image. The method includes determining a respective distance for each of the first feature and the second feature based at least in part on the range data. The method includes determining a velocity associated with a portion of a scene represented by the set of corresponding image features based at least in part on the respective distance for the first feature and the second feature.