Abstract: In general, certain embodiments of the present disclosure provide methods and systems for object detection by a neural network comprising a convolution-nonlinearity step and a recurrent step. In a training mode, a dataset is passed into the neural network, and the neural network is trained to accurately output a box size and a center location of an object of interest. The box size corresponds to the smallest possible bounding box around the object of interest and the center location corresponds to the location of the center of the bounding box. In an inference mode, an image that is not part of the dataset is passed into the neural network. The neural network automatically identifies an object of interest and draws a box around the identified object of interest. The box drawn around the identified object of interest corresponds to the smallest possible bounding box around the object of interest.

Abstract: In general, certain embodiments of the present disclosure provide methods and systems for object detection by a neural network comprising a convolution-nonlinearity step and a recurrent step. In a training mode, a dataset is passed into the neural network, and the neural network is trained to accurately output a box size and a center location of an object of interest. The box size corresponds to the smallest possible bounding box around the object of interest and the center location corresponds to the location of the center of the bounding box. In an inference mode, an image that is not part of the dataset is passed into the neural network. The neural network automatically identifies an object of interest and draws a box around the identified object of interest. The box drawn around the identified object of interest corresponds to the smallest possible bounding box around the object of interest.

Abstract: In general, certain embodiments of the present disclosure provide methods and systems for object detection by a neural network comprising a convolution-nonlinearity step and a recurrent step. In a training mode, a dataset is passed into the neural network, and the neural network is trained to accurately output a box size and a center location of an object of interest. The box size corresponds to the smallest possible bounding box around the object of interest and the center location corresponds to the location of the center of the bounding box. In an inference mode, an image that is not part of the dataset is passed into the neural network. The neural network automatically identifies an object of interest and draws a box around the identified object of interest. The box drawn around the identified object of interest corresponds to the smallest possible bounding box around the object of interest.

Abstract: According to various embodiments, a method for deep-learning based object tracking by a neural network is provided. The method comprises a training mode and an inference mode. In the training mode, the method includes: passing a dataset into the neural network, the dataset including a first image frame and a second image frame; and training the neural network to accurately output a similarity measure for the first and second image frames. In the inference mode, the method includes: passing a plurality of image frames into the neural network, wherein the plurality of image frames is not part of the dataset, the plurality of image frames comprising a first image frame and a second image frame, the first image frame including a first bounding box around an object and the second image frame including a second bounding box around an object; and automatically determining whether the object bounded by the first bounding box is the same object as the object bounded by the second bounding box.

Abstract: According to various embodiments, a method for distance and velocity estimation of detected objects is provided. The method includes receiving an image that includes a minimal bounding box around an object of interest. The method also includes calculating a noisy estimate of the physical position of the object of interest relative to a source of the image. Last, the method includes producing a smooth estimate of the physical position of the object of interest using the noisy estimate.

Abstract: According to various embodiments, a method for gesture recognition using a neural network is provided. The method comprises a training mode and an inference mode. In the training mode, the method includes: passing a dataset into the neural network; and training the neural network to recognize the fingers of a training user and a gesture of interest, wherein the neural network includes a convolution-nonlinearity step and a recurrent step. In the inference mode, the method includes: passing a series of images into the neural network, wherein the series of image is a virtual reality feed that includes the hands of a VR user; and recognizing the fingers of the VR user and gestures of interests from the series of images.

Abstract: According to various embodiments, a method for neural network dataset enhancement is provided. The method comprises taking a first picture using a fixed camera of just a set background, then taking a second picture with the fixed camera. The second picture is taken with the set background and an object of interest in the picture frame. The method further comprises extracting pixels of the image of the object of interest from the second picture, and superimposing the pixels of the image of the object of interest onto a plurality of different images.

Abstract: According to various embodiments, a method for controlling drone movement for object tracking is provided. The method comprises: receiving a position and a velocity of a target; receiving sensor input from a drone; determining an angular velocity and a linear velocity for the drone; and controlling movement of the drone to track the target using the determined angular velocity and linear velocity.