Abstract: Described herein are systems and methods that detect and filter objects from media content. In particular, a detection system accesses classifiers that can detect objects when applied to a video. The detection system receives a text input from a user interface identifying filtering criteria for outputs of the classifiers and inputs the filtering criteria to a filtering language model that produces a filtering object. The detection system presents the filtering object at the user interface. In response to a user accepting the filtering object, the detection system applies the classifiers to the video, which produces a set of detected objects. The detection system applies the filtering object to the detected objects by removing a subset of the detected objects that do not satisfy the filtering criteria and presents the video with the filtered set of detected objects highlighted within the video.

Abstract: Described herein are systems and methods that highlight target objects in media content. The detection system trains a neural network to identify objects within an image. The detection system receives a text input requesting a search within the image and applies a search language model to the text input, which identifies a target object associated with the requested search. The detection system applies the neural network to the image to identify instances of the target object. The detection system modifies a user interface to include the image and modifies the image to highlight the identified instances of the target object. The detection system receives feedback that modifies the highlighted instances of the target object within the user interface and retrains the neural network based on the modified highlighted instances.

Abstract: Described herein are systems and methods that modify media content based on modification criteria extracted from text inputs. A detection system receives a text input that identifies a modification for a video stream and applies a modification language model to the text input, which extracts modification criteria from the text input. The detection system modifies a user interface to include a graphic object identifying the extracted modification criteria and modifies the video stream based on the extracted modification criteria to produce a modified video stream. The detection system modifies the user interface to include the modified video stream. A user may interact with the user interface to produce more graphic objects and further modify the modified video stream.

Abstract: A media detection system receives a video corresponding to a fixed field of view. The media detection system may receive user input indicating one or more object types to identify or a subset of the video within which to identify objects. The media detection system applies one or more machine-learned classifiers to frames of the video and creates a summary video that includes the background of the video and identified instances for simultaneous playback within the fixed field of view. The media detection system may also identify instances of objects in a live video stream and use the identified instances to respond to user questions. The media detection system applies a language model to questions to identify the subject matter of the questions, identifies content within the live video stream associated with the subject matter, and uses the identified content to respond to the user's question.

Abstract: Described herein are systems and methods that highlight target objects in media content. The detection system trains a neural network to identify objects within an image. The detection system receives a text input requesting a search within the image and applies a search language model to the text input, which identifies a target object associated with the requested search. The detection system applies the neural network to the image to identify instances of the target object. The detection system modifies a user interface to include the image and modifies the image to highlight the identified instances of the target object. The detection system receives feedback that modifies the highlighted instances of the target object within the user interface and retrains the neural network based on the modified highlighted instances.

Abstract: A system accesses a first video stream from an internal scanning device (e.g., an X-ray scanner) that scans objects or individuals. It also accesses a second video stream from a capturing device that records a human operator reviewing and interacting with the first stream on a display to identify targeted subject matter. The system then identifies the targeted subject matter based on the operator's interactions and constructs a training dataset based on the identified targeted subject matter. Using this training dataset, the system trains a machine-learning model to identify the targeted subject matter in future video streams from scanning devices.

Abstract: A media detection system receives a video corresponding to a fixed field of view. The media detection system may receive user input indicating one or more object types to identify or a subset of the video within which to identify objects. The media detection system applies one or more machine-learned classifiers to frames of the video and creates a summary video that includes the background of the video and identified instances for simultaneous playback within the fixed field of view. The media detection system may also identify instances of objects in a live video stream and use the identified instances to respond to user questions. The media detection system applies a language model to questions to identify the subject matter of the questions, identifies content within the live video stream associated with the subject matter, and uses the identified content to respond to the user's question.

Abstract: A media detection system receives a video corresponding to a fixed field of view. The media detection system may receive user input indicating one or more object types to identify or a subset of the video within which to identify objects. The media detection system applies one or more machine-learned classifiers to frames of the video and creates a summary video that includes the background of the video and identified instances for simultaneous playback within the fixed field of view. The media detection system may also identify instances of objects in a live video stream and use the identified instances to respond to user questions. The media detection system applies a language model to questions to identify the subject matter of the questions, identifies content within the live video stream associated with the subject matter, and uses the identified content to respond to the user's question.

Abstract: Described herein are systems and methods that detect and filter objects from media content. In particular, a detection system accesses classifiers that can detect objects when applied to a video. The detection system receives a text input from a user interface identifying filtering criteria for outputs of the classifiers and inputs the filtering criteria to a filtering language model that produces a filtering object. The detection system presents the filtering object at the user interface. In response to a user accepting the filtering object, the detection system applies the classifiers to the video, which produces a set of detected objects. The detection system applies the filtering object to the detected objects by removing a subset of the detected objects that do not satisfy the filtering criteria and presents the video with the filtered set of detected objects highlighted within the video.

Abstract: A media detection system receives a video corresponding to a fixed field of view. The media detection system may receive user input indicating one or more object types to identify or a subset of the video within which to identify objects. The media detection system applies one or more machine-learned classifiers to frames of the video and creates a summary video that includes the background of the video and identified instances for simultaneous playback within the fixed field of view. The media detection system may also identify instances of objects in a live video stream and use the identified instances to respond to user questions. The media detection system applies a language model to questions to identify the subject matter of the questions, identifies content within the live video stream associated with the subject matter, and uses the identified content to respond to the user's question.

Abstract: A media detection system receives a video corresponding to a fixed field of view. The media detection system may receive user input indicating one or more object types to identify or a subset of the video within which to identify objects. The media detection system applies one or more machine-learned classifiers to frames of the video and creates a summary video that includes the background of the video and identified instances for simultaneous playback within the fixed field of view. The media detection system may also identify instances of objects in a live video stream and use the identified instances to respond to user questions. The media detection system applies a language model to questions to identify the subject matter of the questions, identifies content within the live video stream associated with the subject matter, and uses the identified content to respond to the user's question.

Abstract: A media detection system receives a video corresponding to a fixed field of view. The media detection system may receive user input indicating one or more object types to identify or a subset of the video within which to identify objects. The media detection system applies one or more machine-learned classifiers to frames of the video and creates a summary video that includes the background of the video and identified instances for simultaneous playback within the fixed field of view. The media detection system may also identify instances of objects in a live video stream and use the identified instances to respond to user questions. The media detection system applies a language model to questions to identify the subject matter of the questions, identifies content within the live video stream associated with the subject matter, and uses the identified content to respond to the user's question.

Abstract: A media detection system receives a video corresponding to a fixed field of view. The media detection system may receive user input indicating one or more object types to identify or a subset of the video within which to identify objects. The media detection system applies one or more machine-learned classifiers to frames of the video and creates a summary video that includes the background of the video and identified instances for simultaneous playback within the fixed field of view. The media detection system may also identify instances of objects in a live video stream and use the identified instances to respond to user questions. The media detection system applies a language model to questions to identify the subject matter of the questions, identifies content within the live video stream associated with the subject matter, and uses the identified content to respond to the user's question.

Abstract: Described herein are systems and methods that search videos and other media content to identify items, objects, faces, or other entities within the media content. Detectors identify objects within media content by, for instance, detecting a predetermined set of visual features corresponding to the objects. Detectors configured to identify an object can be trained using a machine learned model (e.g., a convolutional neural network) as applied to a set of example media content items that include the object. The systems comprise an integrated detection unit configured to record media content, identify preferred content, and communicate the identifications of preferred content for storage in a computationally efficient manner.

Abstract: Described herein are systems and methods that search videos and other media content to identify items, objects, faces, or other entities within the media content. Detectors identify objects within media content by, for instance, detecting a predetermined set of visual features corresponding to the objects. Detectors configured to identify an object can be trained using a machine learned model (e.g., a convolutional neural network) as applied to a set of example media content items that include the object. The systems comprise an integrated detection unit configured to record media content, identify preferred content, and communicate the identifications of preferred content for storage in a computationally efficient manner.

Abstract: Described herein are systems and methods that search videos and other media content to identify items, objects, faces, or other entities within the media content. Detectors identify objects within media content by, for instance, detecting a predetermined set of visual features corresponding to the objects. Detectors configured to identify an object can be trained using a machine learned model (e.g., a convolutional neural network) as applied to a set of example media content items that include the object. The systems provide user interfaces that allow users to review search results, pinpoint relevant portions of media content items where the identified objects are determined to be present, review detector performance and retrain detectors, providing search result feedback, and/or reviewing video monitoring results and analytics.

Abstract: Described herein are systems and methods that search videos and other media content to identify items, objects, faces, or other entities within the media content. Detectors identify objects within media content by, for instance, detecting a predetermined set of visual features corresponding to the objects. Detectors configured to identify an object can be trained using a machine learned model (e.g., a convolutional neural network) as applied to a set of example media content items that include the object. The systems comprise an integrated detection unit configured to record media content, identify preferred content, and communicate the identifications of preferred content for storage in a computationally efficient manner.

Abstract: Described herein are systems and methods that search videos and other media content to identify items, objects, faces, or other entities within the media content. Detectors identify objects within media content by, for instance, detecting a predetermined set of visual features corresponding to the objects. Detectors configured to identify an object can be trained using a machine learned model (e.g., a convolutional neural network) as applied to a set of example media content items that include the object. The systems comprise an integrated detection unit configured to record media content, identify preferred content, and communicate the identifications of preferred content for storage in a computationally efficient manner.

Abstract: Described herein are systems and methods that search videos and other media content to identify items, objects, faces, or other entities within the media content. Detectors identify objects within media content by, for instance, detecting a predetermined set of visual features corresponding to the objects. Detectors configured to identify an object can be trained using a machine learned model (e.g., a convolutional neural network) as applied to a set of example media content items that include the object. The systems and methods describe techniques that search videos and media content to determine the presence of unknown objects, generate novel detectors trained to identify the unknown objects, and apply the novel detectors to historical media content to identify previous appearances of the unknown objects.

Abstract: Described herein are systems and methods that search videos and other media content to identify items, objects, faces, or other entities within the media content. Detectors identify objects within media content by, for instance, detecting a predetermined set of visual features corresponding to the objects. Detectors configured to identify an object can be trained using a machine learned model (e.g., a convolutional neural network) as applied to a set of example media content items that include the object. The systems comprise an integrated detection unit configured to record media content, identify preferred content, and communicate the identifications of preferred content for storage in a computationally efficient manner.