Abstract: Example apparatus disclosed herein are to process a first image of a first video segment from the image capture sensor with a machine learning algorithm to determine a first score for the first image, the machine learning algorithm to detect actions associated with images, the actions associated with labels. Disclosed example apparatus are also to determine a second score for the first video segment based on respective first scores for corresponding images in the first video segment. Disclosed example apparatus are further to determine, based on the second score, whether to retain the first video segment in the memory.

Abstract: Example apparatus disclosed herein are to process a first image of a first video segment from the image capture sensor with a machine learning algorithm to determine a first score for the first image, the machine learning algorithm to detect actions associated with images, the actions associated with labels. Disclosed example apparatus are also to determine a second score for the first video segment based on respective first scores for corresponding images in the first video segment. Disclosed example apparatus are further to determine, based on the second score, whether to retain the first video segment in the memory.

Abstract: An embodiment includes at least one computer readable storage medium comprising instructions that when executed enable a system to: receive (a)(i) first radio signal location data for a first object from a radio sensor; and (a)(ii) first visual signal location data for the first object from a camera sensor; perform feature extraction on (b)(i) the first radio signal location data to determine first extracted radio signal features; and (b)(ii) the first visual signal location data to determine first extracted visual signal features; solve a first association problem between the first extracted radio signal features and the first extracted visual signal features to determine first fused location data; and store the first fused location data in the at least one computer readable storage medium. Other embodiments are described herein.

Abstract: A method of predicting a trajectory of a target vehicle with respect to an ego vehicle includes receiving a first image having a target object representing a target vehicle on a road from a camera mounted on the ego vehicle, generating a bounding box associated with the target vehicle, the bounding box surrounding a contour of the target object, extracting tire contact information of the target object with a road surface from the first image, wherein each tire contact information represents coordinates, in the first image, of contact between a corresponding tire and the road, mapping the plurality of tire contact information from the first image to a bird's eye view image of the first image, and calculating the trajectory, with reference to a moving direction of the ego vehicle, of the target object using the mapped tire contact information on the bird's eye view image.

Abstract: A region (e.g., a portion) of a full-size image is selected for analysis by a vision inference model. The region of the image may be dynamically selected for the image based on environmental characteristics of the image. These environmental characteristics may include semantic information about the environment that may be determined from a previous image of the environment, road structure information, control information, and motion information. These environmental characteristics may thus describe expected areas of interest in the image. The selected region of the full-size image is resized to a size compatible with a vision inference model which identifies semantic information about the current image. This semantic information may then be used to select a region of a subsequent image.

Abstract: Example apparatus disclosed herein are to process a first image of a first video segment from the image capture sensor with a machine learning algorithm to determine a first score for the first image, the machine learning algorithm to detect actions associated with images, the actions associated with labels. Disclosed example apparatus are also to determine a second score for the first video segment based on respective first scores for corresponding images in the first video segment. Disclosed example apparatus are further to determine, based on the second score, whether to retain the first video segment in the memory.

Abstract: An embodiment includes at least one computer readable storage medium comprising instructions that when executed enable a system to: receive (a)(i) first radio signal location data for a first object from a radio sensor; and (a)(ii) first visual signal location data for the first object from a camera sensor; perform feature extraction on (b)(i) the first radio signal location data to determine first extracted radio signal features; and (b)(ii) the first visual signal location data to determine first extracted visual signal features; solve a first association problem between the first extracted radio signal features and the first extracted visual signal features to determine first fused location data; and store the first fused location data in the at least one computer readable storage medium. Other embodiments are described herein.

Abstract: An apparatus for video summarization using sematic information is described herein. The apparatus includes a controller, a scoring mechanism, and a summarizer. The controller is to segment an incoming video stream into a plurality of activity segments, wherein each frame is associated with an activity. The scoring mechanism is to calculate a score for each frame of each activity, wherein the score is based on a plurality of objects in each frame. The summarizer is to summarize the activity segments based on the score for each frame.

Abstract: An embodiment includes at least one computer readable storage medium comprising instructions that when executed enable a system to: receive (a)(i) first radio signal location data for a first object from a radio sensor; and (a)(ii) first visual signal location data for the first object from a camera sensor; perform feature extraction on (b)(i) the first radio signal location data to determine first extracted radio signal features; and (b)(ii) the first visual signal location data to determine first extracted visual signal features; solve a first association problem between the first extracted radio signal features and the first extracted visual signal features to determine first fused location data; and store the first fused location data in the at least one computer readable storage medium. Other embodiments are described herein.

Abstract: An embodiment includes at least one computer readable storage medium comprising instructions that when executed enable a system to: receive (a)(i) first radio signal location data for a first object from a radio sensor; and (a)(ii) first visual signal location data for the first object from a camera sensor; perform feature extraction on (b)(i) the first radio signal location data to determine first extracted radio signal features; and (b)(ii) the first visual signal location data to determine first extracted visual signal features; solve a first association problem between the first extracted radio signal features and the first extracted visual signal features to determine first fused location data; and store the first fused location data in the at least one computer readable storage medium. Other embodiments are described herein.

Abstract: An apparatus for video summarization using sematic information is described herein. The apparatus includes a controller, a scoring mechanism, and a summarizer. The controller is to segment an incoming video stream into a plurality of activity segments, wherein each frame is associated with an activity. The scoring mechanism is to calculate a score for each frame of each activity, wherein the score is based on a plurality of objects in each frame. The summarizer is to summarize the activity segments based on the score for each frame.

Abstract: A region (e.g., a portion) of a full-size image is selected for analysis by a vision inference model. The region of the image may be dynamically selected for the image based on environmental characteristics of the image. These environmental characteristics may include semantic information about the environment that may be determined from a previous image of the environment, road structure information, control information, and motion information. These environmental characteristics may thus describe expected areas of interest in the image. The selected region of the full-size image is resized to a size compatible with a vision inference model which identifies semantic information about the current image. This semantic information may then be used to select a region of a subsequent image.

Abstract: An apparatus for visual search and retrieval using sematic information is described herein. The apparatus includes a controller, a scoring mechanism, an extractor, and a comparator. The controller is to segment an incoming video stream into a plurality of activity segments, wherein each frame is associated with an activity. The scoring mechanism is to calculate a score for each segment, wherein the score is based, at least partially, on a classification probability of each frame. The extractor is to extract deep features from a highest ranked segment, and the comparator is to determine the top-K neighbors based on the deep features.

Abstract: An apparatus for video summarization using semantic information is described herein. The apparatus includes a controller, a scoring mechanism, and a summarizer. The controller is to segment an incoming video stream into a plurality of activity segments, wherein each frame is associated with an activity. The scoring mechanism is to calculate a score for each frame of each activity, wherein the score is based on a plurality of objects in each frame. The summarizer is to summarize the activity segments based on the score for each frame.

Abstract: An embodiment includes at least one computer readable storage medium comprising instructions that when executed enable a system to: receive (a)(i) first radio signal location data for a first object from a radio sensor; and (a)(ii) first visual signal location data for the first object from a camera sensor; perform feature extraction on (b)(i) the first radio signal location data to determine first extracted radio signal features; and (b)(ii) the first visual signal location data to determine first extracted visual signal features; solve a first association problem between the first extracted radio signal features and the first extracted visual signal features to determine first fused location data; and store the first fused location data in the at least one computer readable storage medium. Other embodiments are described herein.

Abstract: Embodiments of the present disclosure provide techniques and configurations for a system to manage a home environment. In one instance, an apparatus for managing a home environment may be configured to detect variations in parameters of signals provided by RFID tags disposed in the home environment to monitor the home environment. The apparatus may identify one or more conditions of an area of the home environment in which the RFID tags are disposed, based at least in part on the variations in the one or more parameters and respective locations of the RFID tags in the area of the home environment. The apparatus may select one or more actions, from a plurality of actions associated with the home environment, based at least in part on the identified conditions. The selected actions may be performed to respond to the identified conditions. Other embodiments may be described and/or claimed.

Abstract: Embodiments of the present disclosure provide techniques and configurations for a system to manage a home environment. In one instance, an apparatus for managing a home environment may be configured to detect variations in parameters of signals provided by RFID tags disposed in the home environment to monitor the home environment. The apparatus may identify one or more conditions of an area of the home environment in which the RFID tags are disposed, based at least in part on the variations in the one or more parameters and respective locations of the RFID tags in the area of the home environment. The apparatus may select one or more actions, from a plurality of actions associated with the home environment, based at least in part on the identified conditions. The selected actions may be performed to respond to the identified conditions. Other embodiments may be described and/or claimed.

Abstract: An apparatus for visual search and retrieval using sematic information is described herein. The apparatus includes a controller, a scoring mechanism, an extractor, and a comparator. The controller is to segment an incoming video stream into a plurality of activity segments, wherein each frame is associated with an activity. The scoring mechanism is to calculate a score for each segment, wherein the score is based, at least partially, on a classification probability of each frame. The extractor is to extract deep features from a highest ranked segment, and the comparator is to determine the top-K neighbors based on the deep features.

Abstract: An embodiment includes at least one computer readable storage medium comprising instructions that when executed enable a system to: receive (a)(i) first radio signal location data for a first object from a radio sensor; and (a)(ii) first visual signal location data for the first object from a camera sensor; perform feature extraction on (b)(i) the first radio signal location data to determine first extracted radio signal features; and (b)(ii) the first visual signal location data to determine first extracted visual signal features; solve a first association problem between the first extracted radio signal features and the first extracted visual signal features to determine first fused location data; and store the first fused location data in the at least one computer readable storage medium. Other embodiments are described herein.

Abstract: Techniques for a system, article, and method of low-complexity histogram of gradients generation for image processing may include histogram of gradients generation for image processing including the following operations: obtaining image data including horizontal and vertical gradient components of individual pixels of an image; associating the horizontal and vertical gradient components of the same pixel with one of a plurality of angular channels depending on the values of the horizontal and vertical gradient components; determining a gradient magnitude and a gradient orientation of individual angular channels after the horizontal and vertical gradient components are assigned to the channels; and generating a histogram of gradients by using the gradient direction and gradient magnitude of the angular channels.