7203 results about "Object detection" patented technology

A camera system comprises an image capturing device, object detection module, object tracking module, and match classifier. The object detection module receives image data and detects objects appearing in one or more of the images. The object tracking module temporally associates instances of a first object detected in a first group of the images. The first object has a first signature representing features of the first object. The match classifier matches object instances by analyzing data derived from the first signature of the first object and a second signature of a second object detected in a second image. The second signature represents features of the second object derived from the second image. The match classifier determine whether the second signature matches the first signature. A training process automatically configures the match classifier using a set of possible object features.

An object recognition apparatus that can recognize an object in an image acquired by an on-vehicle camera mounted on a vehicle with high efficiency and accuracy. A recognition processing is carried out in a cut off range, which is cut off from the image, and in which an object to be recognized is supposed to be present. The cut off range is obtained using support information supplied from a position detection device and/or an anti-lock brake device of the vehicle. Since the object in the image changes its position as the vehicle moves, the displacement must be estimated during the object recognition. The estimation requires camera attitude parameters of the on-vehicle camera with respect to the road, and the estimation is performed using support information supplied from the position detection device and/of the anti-lock brake device. This will improve the efficiency and accuracy of obtaining the camera attitude parameters.

A waveform adaptive transmitter that conditions and/or modulates the phase, frequency, bandwidth, amplitude and/or attenuation of ultra-wideband (UWB) pulses. The transmitter confines or band-limits UWB signals within spectral limits for use in communication, positioning, and/or radar applications. One embodiment comprises a low-level UWB source (e.g., an impulse generator or time-gated oscillator (fixed or voltage-controlled)), a waveform adapter (e.g., digital or analog filter, pulse shaper, and/or voltage variable attenuator), a power amplifier, and an antenna to radiate a band-limited and/or modulated UWB or wideband signals. In a special case where the oscillator has zero frequency and outputs a DC bias, a low-level impulse generator impulse-excites a bandpass filter to produce an UWB signal having an adjustable center frequency and desired bandwidth based on a characteristic of the filter. In another embodiment, a low-level impulse signal is approximated by a time-gated continuous-wave oscillator to produce an extremely wide bandwidth pulse with deterministic center frequency and bandwidth characteristics. The UWB signal may be modulated to carry multi-megabit per second digital data, or may be used in object detection or for ranging applications. Activation of the power amplifier may be time-gated in cadence with the UWB source thereby to reduce inter-pulse power consumption. The UWB transmitter is capable of extremely high pulse repetition frequencies (PRFs) and data rates in the hundreds of megabits per second or more, frequency agility on a pulse-to-pulse basis allowing frequency hopping if desired, and extensibility from below HF to millimeter wave frequencies.

A vehicle awareness system for monitoring remote vehicles relative to a host vehicle. The vehicle awareness system includes at least one object sensing device and a vehicle-to-vehicle communication device. A data collection module is provided for obtaining a sensor object data map and vehicle-to-vehicle object data map. A fusion module merges the sensor object data map and vehicle-to-vehicle object data map for generating a cumulative object data map. A tracking module estimates the relative position of the remote vehicles to the host vehicle.

A method and system for anatomical object detection using marginal space deep neural networks is disclosed. The pose parameter space for an anatomical object is divided into a series of marginal search spaces with increasing dimensionality. A respective sparse deep neural network is trained for each of the marginal search spaces, resulting in a series of trained sparse deep neural networks. Each of the trained sparse deep neural networks is trained by injecting sparsity into a deep neural network by removing filter weights of the deep neural network.

A wireless energy transfer system includes a foreign object debris detection system. The system includes at least one wireless energy transfer source configured to generate an oscillating magnetic field. The foreign object debris may be detected by at least one field gradiometer positioned in the oscillating magnetic field. The voltage of the at least one field gradiometer may be measured using readout circuitry and a feedback loop based on the readings from the gradiometers may be used to control the parameters of the wireless energy source.

An imaging system for a vehicle includes an imaging array sensor and a control. The image array sensor comprises a plurality of photo-sensing pixels and is positioned at the vehicle with a field of view exteriorly of the vehicle. The imaging array sensor is operable to capture an image of a scene occurring exteriorly of the vehicle. The captured image comprises an image data set representative of the exterior scene. The control algorithmically processes the image data set to a reduced image data set of the image data set. The control processes the reduced image data set to extract information from the reduced image data set. The control selects the reduced image data set based on a steering angle of the vehicle.

Aspects of the present disclosure relate generally to modeling a vehicle's view of its environment. This view need not include what objects or features the vehicle is actually seeing, but rather those areas that the vehicle is able to observe using its sensors if the sensors were completely un-occluded. For example, for each of a plurality of sensors of the object detection component, a computer may an individual 3D model of that sensor's field of view. Weather information is received and used to adjust one or more of the models. After this adjusting, the models may be aggregated into a comprehensive 3D model. The comprehensive model may be combined with detailed map information indicating the probability of detecting objects at different locations. A model of the vehicle's environment may be computed based on the combined comprehensive 3D model and detailed map information and may be used to maneuver the vehicle.

A video surveillance system uses rule-based reasoning and multiple-hypothesis scoring to detect predefined behaviors based on movement through zone patterns. Trajectory hypothesis spawning allows for trajectory splitting and/or merging and includes local pruning to managed hypothesis growth. Hypotheses are scored based on a number of criteria, illustratively including at least one non-spatial parameter. Connection probabilities computed during the hypothesis spawning process are based on a number of criteria, illustratively including object size. Object detection and probability scoring is illustratively based on object class.

A method and system for anatomical object detection using marginal space deep neural networks is disclosed. The pose parameter space for an anatomical object is divided into a series of marginal search spaces with increasing dimensionality. A respective deep neural network is trained for each of the marginal search spaces, resulting in a series of trained deep neural networks. Each of the trained deep neural networks can evaluate hypotheses in a current parameter space using discriminative classification or a regression function. An anatomical object is detected in a medical image by sequentially applying the series of trained deep neural networks to the medical image.

Disclosed systems and methods relate to remote sensing, deep learning, and object detection. Some embodiments relate to machine learning for object detection, which includes, for example, identifying a class of pixel in a target image and generating a label image based on a parameter set. Other embodiments relate to machine learning for geometry extraction, which includes, for example, determining heights of one or more regions in a target image and determining a geometric object property in a target image. Yet other embodiments relate to machine learning for alignment, which includes, for example, aligning images via direct or indirect estimation of transformation parameters.

A method for determining the orientation of a digital image, includes the steps of: employing a semantic object detection method to detect the presence and orientation of a semantic object; employing a scene layout detection method to detect the orientation of a scene layout; and employing an arbitration method to produce an estimate of the image orientation from the orientation of the detected semantic object and the detected orientation of the scene layout.

The present invention detects a moving object by generating the distance information of the moving object, detecting the object motion, determining the object distance, detecting the object image area and the object contour from the video image that includes the object image and contour, and provides a moving object detection apparatus to carry out such detection as well as detecting a contour of the specific moving object by detecting the center of the moving object in high precision.

A computer-implemented method executed by at least one processor for training fast models for real-time object detection with knowledge transfer is presented. The method includes employing a Faster Region-based Convolutional Neural Network (R-CNN) as an objection detection framework for performing the real-time object detection, inputting a plurality of images into the Faster R-CNN, and training the Faster R-CNN by learning a student model from a teacher model by employing a weighted cross-entropy loss layer for classification accounting for an imbalance between background classes and object classes, employing a boundary loss layer to enable transfer of knowledge of bounding box regression from the teacher model to the student model, and employing a confidence-weighted binary activation loss layer to train intermediate layers of the student model to achieve similar distribution of neurons as achieved by the teacher model.

An interruption free navigator includes an inertial measurement unit, a north finder, a velocity producer, a positioning assistant, a navigation processor, an altitude measurement, an object detection system, a wireless communication device, and a display device and map database. Output signals of the inertial measurement unit, the velocity producer, the positioning assistant, the altitude measurement, the object detection system, and the north finder are processed to obtain highly accurate position measurements of the person. The user's position information can be exchanged with other users through the wireless communication device, and the location and surrounding information can be displayed on the display device by accessing a map database with the person position information.

Object recognition apparatus and methods useful for extracting information from an input signal. In one embodiment, the input signal is representative of an element of an image, and the extracted information is encoded into patterns of pulses. The patterns of pulses are directed via transmission channels to a plurality of detector nodes configured to generate an output pulse upon detecting an object of interest. Upon detecting a particular object, a given detector node elevates its sensitivity to that particular object when processing subsequent inputs. In one implementation, one or more of the detector nodes are also configured to prevent adjacent detector nodes from generating detection signals in response to the same object representation. The object recognition apparatus modulates properties of the transmission channels by promoting contributions from channels carrying information used in object recognition.

A vehicle has a plurality of control apparatuses, a user input, a geographic position component, an object detection apparatus, memory, and a display. A processor is also included and is programmed to receive the destination information, identify a route, and determine the current geographic location of the vehicle. The processor is also programmed to identify an object and object type based on object information received from the object detection apparatus and to determine at least one warning characteristic of the identified object based on at least one of: the object type, a detected proximity of the detected object to the vehicle, the location of the detected object relative to predetermined peripheral areas of the vehicle, the current geographic location of the vehicle, and the route. The processor is also configured to select and display on the display an object warning image based on the at least one warning characteristic.

PCT No. PCT/US95/02155 Sec. 371 Date Dec. 22, 1997 Sec. 102(e) Date Dec. 22, 1997 PCT Filed Feb. 21, 1995 PCT Pub. No. WO95/22804 PCT Pub. Date Aug. 24, 1995A method and ultra-compact system has been developed for illuminating and detecting the surface topography of an object such as the finger (4) of an individual. The system (8) is capable of producing high-contrast images which can be electronically transmitted in real-time, or stored using electronic or photographic recording devices. Light traveling within a light transmitting substrate (2) is redirected by a slanted-fringed light diffractive grating preferably embodied within a volume hologram (3). The volume hologram (3), either of the reflection or transmission type, is attached to the light transmitting substrate (2). and functions to diffract light striking thereupon and illuminate an object having topographical surface structure. After being spatially and intensity modulated in accordance with topographical details of the illuminated object, the insulated light passes back through the light transmitting substrate (2) and the volume hologram (3), onto an image detection array. for subsequent analysis. Each of the disclosed embodiments has a compact geometry suitable for use in diverse object identification applications.