Systems and methods for improved operations of ski lifts increase skier safety at on-boarding and off-boarding locations by providing an always-on, always-alert system that “watches” these locations, identifies developing problem situations, and initiates mitigation actions. One or more video cameras feed live video to a video processing module. The video processing module feeds resulting sequences of images to an artificial intelligence (AI) engine. The AI engine makes an inference regarding existence of a potential problem situation based on the sequence of images. This inference is fed to an inference processing module, which determines if the inference processing module should send an alert or interact with the lift motor controller to slow or stop the lift.

In one embodiment, a processing device receives a first digital fingerprint of a media item along with a first content management rule. The processing device separately receives a second digital fingerprint of the media item along with a second content management rule. The processing device determines that the received digital fingerprints are for the same media item based upon a match between the first digital fingerprint and the second digital fingerprint. The processing device determines that all rights to the media item have been accounted for, and then determines a set of actions to be performed for hosted media items comprising the media item based at least in part upon the first content management rule and the second content management rule. Processing logic may also perform a conflict resolution process for conflicting rights claims to the media item.

The present application discloses a video type detection method, apparatus, electronic device and storage medium. A specific implementation solution is as follows: obtaining N key frames of a first video, where N is an integer greater than 1, and a type of the first video is to be detected; obtaining M confidence scores corresponding to each of the N key frames by inputting each of the N key frames into M algorithm models corresponding to the first video type respectively, where M is an integer greater than 1; determining a confidence score of the first video by a fusion strategy algorithm model according to N×M confidence scores of the N key frames; and comparing the confidence score of the first video with a confidence score threshold corresponding to a first video type, to determine whether the type of the first video is the first video type or not.

An analytics device for monitoring maintenance on an elevator system performed by an individual including: a processor; and a memory including computer-executable instructions that, when executed by the processor, cause the processor to perform operations, the operations including: capturing a first video stream using a first video camera; extracting sequences from at least the first video stream; extracting features from the sequences; and analyzing, using a long short-term memory model, the sequence to determine whether the maintenance performed on the elevator system by the individual is performed correctly.

A computer that identifies the video. The computer annotates the video using a deep learning tool. The computer analyzes the annotated video to highlight a dangerous condition. The computer identifies a video from a repository with the dangerous condition. The computer analyzes the video and the video from the repository using a similarity analysis. The computer determines a score based on the annotated video and based on comparing the video to the video from the repository and based on determining the score is above a threshold value, the computer generates an action.

The embodiments of the present disclosure provide a method of registering a face based on video data, including: receiving video data; acquiring a first image frame sequence from the video data, wherein each image frame in the first image frame sequence includes a face detection frame containing a complete facial feature; determining whether each image frame reaches a preset definition or not according to a relative position of the face detection frame in the image frame; extracting a plurality of sets of facial features based on an image information of the plurality of face detection frames in response to determining that the image frame reaches the preset definition, and determining whether the faces represent an object or not according to the plurality of sets of facial features; and registering the object according to the first image frame sequence in response to determining that the faces represent the object.

Embodiments of the present application disclose a method and apparatus for determining an object adding mode, an electronic device and a medium. A specific implementation of the method includes: obtaining a target video and a to-be-added object set to be added to a target video; determining time periods corresponding to storyboards of the target video; determining a presentation period of a to-be-added object in the to-be-added object set in the target video based on the time periods corresponding to the storyboards; generating object adding indication information based on the determined presentation period, where the object adding indication information includes time prompt information used to indicate the presentation period of the to-be-added object in the to-be-added object set in the target video. According to this implementation, automatic selection of time for adding the to-be-added object to the target video is realized.

A system and method for identifying risk in warehouse environments includes video sensors configured to capture video streams and a central processing unit communicatively coupled to video sensors. The central processing unit is configured with an emerging risk discovery unit configured to detect a current risk subject in the obtained plurality of real time video frames. Further, the plurality of real time video frames are stored in a memory. A location of the current risk subject detected in the obtained plurality of real time video frames is detected. Further, physical characteristics of current risk subject for predicting one or more actions performed by the current risk subject are estimated, and actions and location of the current risk subject are processed to detect patterns or movements and activities undertaken by one or more risk subjects.

Systems, methods, and non-transitory computer-readable medium storing instructions that, when executed, causes a processor to perform operations to display a three-dimensional (3D) space. The methods may include, with an imaging device, capturing a first series of frames as the imaging device travels from a first location to a second location within a space, and capturing a second series of frames as the imaging device travels from the second location to the first location. The method may also include determining a first segment in the first series of frames that matches a second segment in the second series of frames to create a segmentation dataset, generating video clip data based on the segmentation dataset, the video clip data defining a series of video clips, and displaying the series of video clips.

Embodiments of the present invention provide a method, system and computer program product for exciting moment notification for a live event. An exciting moment notification method includes first defining in a table in memory of a computer, an exciting moment occurring during a live event, such as a card game, awards program or a sporting event, and then annotating a multiplicity of images of different instances of the live event with the exciting moment. The method further includes video streaming a live instance of the live event over a computer communications network and matching a frame of the video streamed live instance in the memory of the computer to an annotated one of the multiplicity of images. Finally, the method includes transmitting a message to at least one subscriber of the instance of the live event indicating an occurrence of the exciting moment.