Abstract: In general, certain embodiments of the present disclosure provide a detection and avoidance system for a vehicle. According to various embodiments, the detection and avoidance system comprises an imaging unit configured to obtain a first image of a field of view at a first camera channel. The first camera channel filters radiation at a wavelength, where one or more objects in the field of view do not emit radiation at the wavelength. The detection and avoidance system further comprises a processing unit configured to receive the first image from the imaging unit and to detect one or more objects therein, as well as a notifying unit configured to communicate collision hazard information determined based upon the detected one or more objects to a pilot control system of the vehicle. Accordingly, the pilot control maneuvers the vehicle to avoid the detected objects.

Abstract: A method for classifying at least one object of interest in a video is provided. The method includes accessing, using at least one processing device, a frame of the video, the frame including at least one object of interest to be classified, performing, using the at least one processing device, object detection on the frame to detect the object of interest, tracking, using the at least one processing device, the object of interest over a plurality of frames in the video over time using a persistent tracking capability, isolating, using the at least one processing device, a segment of the frame that includes the object of interest, classifying, using the at least one processing device, the object of interest by processing the segment using deep learning, and generating an output that indicates the classification of the object of interest.

Abstract: In general, certain embodiments of the present disclosure provide a detection and avoidance system for a vehicle. According to various embodiments, the detection and avoidance system comprises an imaging unit configured to obtain a first image of a field of view at a first camera channel. The first camera channel filters radiation at a wavelength, where one or more objects in the field of view do not emit radiation at the wavelength. The detection and avoidance system further comprises a processing unit configured to receive the first image from the imaging unit and to detect one or more objects therein, as well as a notifying unit configured to communicate collision hazard information determined based upon the detected one or more objects to a pilot control system of the vehicle. Accordingly, the pilot control maneuvers the vehicle to avoid the detected objects.

Abstract: In general, certain embodiments of the present disclosure provide a detection and avoidance system for a vehicle. According to various embodiments, the detection and avoidance system comprises an imaging unit configured to obtain a first image of a field of view at a first camera channel. The first camera channel filters radiation at a wavelength, where one or more objects in the field of view do not emit radiation at the wavelength. The detection and avoidance system further comprises a processing unit configured to receive the first image from the imaging unit and to detect one or more objects therein, as well as a notifying unit configured to communicate collision hazard information determined based upon the detected one or more objects to a pilot control system of the vehicle. Accordingly, the pilot control maneuvers the vehicle to avoid the detected objects.

Abstract: Systems and methods for coded visual markers in a surveillance system. An exemplary system includes a camera to capture images of a secure area, memory to store identifiers for persons that are authorized to be in the secure area, and a processor communicatively coupled with the camera and the memory. The processor analyzes the images to detect a person in the secure area and detects a pattern of a light source at the person. The processor then decodes the pattern of the light source and authorizes the person in the secure area based on a match between the decoded pattern of the light source and an identifier stored in the memory of one of the persons authorized to be in the secure area.

Abstract: In general, certain embodiments of the present disclosure provide a detection and avoidance system for a vehicle. According to various embodiments, the detection and avoidance system comprises an imaging unit configured to obtain a first image of a field of view at a first camera channel. The first camera channel filters radiation at a wavelength, where one or more objects in the field of view do not emit radiation at the wavelength. The detection and avoidance system further comprises a processing unit configured to receive the first image from the imaging unit and to detect one or more objects therein, as well as a notifying unit configured to communicate collision hazard information determined based upon the detected one or more objects to a pilot control system of the vehicle. Accordingly, the pilot control maneuvers the vehicle to avoid the detected objects.

Abstract: Systems and methods for coded visual markers in a surveillance system. An exemplary system includes a camera to capture images of a secure area, memory to store identifiers for persons that are authorized to be in the secure area, and a processor communicatively coupled with the camera and the memory. The processor analyzes the images to detect a person in the secure area and detects a pattern of a light source at the person. The processor then decodes the pattern of the light source and authorizes the person in the secure area based on a match between the decoded pattern of the light source and an identifier stored in the memory of one of the persons authorized to be in the secure area.

Abstract: A computing device for detecting an object of interest in an image is provided. The computing device includes a memory device and a processor to process image data by evaluating pixel contrast to facilitate detecting an edge transition in an image, by identifying neighboring pixels that are above a predetermined contrast value, which represent a potential object of interest, filtering the pixels based on color information to identify areas where there is a color edge transition, applying a convolution filter mask to yield regions of pixels having a localized concentration of contrast and color changes, applying a threshold filter for identifying those pixel regions having values that are greater than a predetermined threshold value and grouping the identified pixel regions into one or more groups representing an object of interest, and generating an alert to notify an operator that the object of interest in the image has been detected.

Abstract: A method for classifying at least one object of interest in a video is provided. The method includes accessing, using at least one processing device, a frame of the video, the frame including at least one object of interest to be classified, performing, using the at least one processing device, object detection on the frame to detect the object of interest, tracking, using the at least one processing device, the object of interest over a plurality of frames in the video over time using a persistent tracking capability, isolating, using the at least one processing device, a segment of the frame that includes the object of interest, classifying, using the at least one processing device, the object of interest by processing the segment using deep learning, and generating an output that indicates the classification of the object of interest.

Abstract: A method for classifying at least one object of interest in a video is provided. The method includes accessing, using at least one processing device, a frame of the video, the frame including at least one object of interest to be classified, performing, using the at least one processing device, object detection on the frame to detect the object of interest, tracking, using the at least one processing device, the object of interest over a plurality of frames in the video over time using a persistent tracking capability, isolating, using the at least one processing device, a segment of the frame that includes the object of interest, classifying, using the at least one processing device, the object of interest by processing the segment using deep learning, and generating an output that indicates the classification of the object of interest.

Abstract: A method for classifying at least one object of interest in a video is provided. The method includes accessing, using at least one processing device, a frame of the video, the frame including at least one object of interest to be classified, performing, using the at least one processing device, object detection on the frame to detect the object of interest, tracking, using the at least one processing device, the object of interest over a plurality of frames in the video over time using a persistent tracking capability, isolating, using the at least one processing device, a segment of the frame that includes the object of interest, classifying, using the at least one processing device, the object of interest by processing the segment using deep learning, and generating an output that indicates the classification of the object of interest.

Abstract: A computing device for detecting an object of interest in an image is provided. The computing device includes a memory device and a processor to process image data by evaluating pixel contrast to facilitate detecting an edge transition in an image, by identifying neighboring pixels that are above a predetermined contrast value, which represent a potential object of interest, filtering the pixels based on color information to identify areas where there is a color edge transition, applying a convolution filter mask to yield regions of pixels having a localized concentration of contrast and color changes, applying a threshold filter for identifying those pixel regions having values that are greater than a predetermined threshold value and grouping the identified pixel regions into one or more groups representing an object of interest, and generating an alert to notify an operator that the object of interest in the image has been detected.

Abstract: System and method for determining the height of an object reflecting a real beam radar signal by receiving the reflected signal with a first and second vertically spaced antennas. Receivers provide two signals indicating the phase of the reflected signals received by the two antennas. A digital processor determines the phase difference between the two signals and calculates the object height.

Abstract: Methods, apparatus, and computer program products are provided for tracking at least one moving target with a radar device without requiring the use of Doppler information. The invention comprises scanning an area with radar signals at a first time to receive a first plurality of target data signals indicative of a position of the target at the first time and determining the position of the target at the first time by collecting the first plurality of target data signals into a first target data grouping, such that the first target data grouping defines a first reference point. Similarly, a second reference point for the target is determined for a second time, and the position of the first reference point is compared to the position of the second reference point to track the moving target. Advantageously, the tracked positions of the moving target may be used to predict a future position of the target at a subsequent time.

Abstract: Methods, apparatus, and computer program products are provided for improving the crossrange resolution of a radar device for more accurate determinations of angular position of a target. The radar device transmits and receives scanning signals at crossrange beam positions having a beamwidth, wherein the beam positions are separated by a step. The radar device scans a target to receive target data from at least three beam positions, typically consecutive beam positions, to determine a first beam position with a first target data value that is greater than a second target data value of a second beam position preceding the first beam position and greater than a third target data value of a third beam position following the first beam position. A target data relationship between the first, second, and third beam positions is determined to provide the angular position of the target relative to the first beam position.