50 results about "Video copy detection" patented technology

Content streams may be segmented to provide automatic extraction and storage of content items without intervening commercials or other unrelated content. These content items may then be stored in a database and made accessible to subscribers through, for example, an on-demand service. Automatic segmentation may include the identification of program boundaries, segmentation of a content stream based on the boundaries and the subsequent classification of the segments into content types. For example, audio and video duplication detection may be used to identify commercials since commercials tend to repeat frequently over a relatively short amount of time. A system may further identify an end of program indicator in a video stream to determine when a program ends. Accordingly, if a program ends after a scheduled end time, a recording device (e.g., the program is being recorded) may automatically extend the recording time to capture the entire program.

A two-step matching technique is embodied in a video-copy-detection algorithm that detects copies of video sequences. The two-step matching technique uses ordinal signatures of frame partitions and their differences from partition mean values. The algorithm of this invention is not only robust to intensity / color variations it can also effectively handle various format conversions, thereby providing robustness regardless of the video dynamics of the frame shots.

The invention belongs to the technical field of the video processing, and particularly relates to a method for identifying content of a digital video, which mainly comprises a key frame extraction module, a video copy detection module and a video frame content identification module. The video copy detection module adopts a video copy detection method based on local ordering to ensure that a similar frame is rapidly obtained through a given possibility, and to improve the detection performance. The video frame content identification module adopts a pornographic image identification algorithm, the algorithm comprises four weak classifiers (skin area proportion classifier, face quantity classifier, invariant-moment classifier and skin area distribution classifier), the four weak classifiers are integrated to form one strong classifier, the result of the strong classifier is the final detection result, so the mistake recognition rate and nonrecognition rate can be effectively reduced. The method has wide application value for the internet digital video information monitoring.

The invention discloses a video copy detection method based on multi-feature Hash, which mainly solves the problem that detection efficiency and detection accuracy cannot be effectively balanced in the exiting video copy detection algorithm. The video copy detection method based on multi-feature Hash comprises the following realization steps of: (1) extracting the pyramid histogram of oriented gradients (PHOG) of a key frame as the global feature of the key frame; (2) extracting a weighted contrast histogram based on scale invariant feature transform (SIFT) of the key frame as the local feature of the key frame; (3) establishing a target function by a similarity-preserving multi-feature Hash learning SPM2H algorithm, and obtaining L Hash functions by optimization solution; (4) mapping the key frame of a database video and the key frame of an inquired video into an L-dimensional Hash code by virtue of the L Hash functions; (5) judging whether the inquired video is the copied video or not through feature matching. The video copy detection method based on multi-feature Hash disclosed by the invention is good in robustness for multiple attacks, and capable of being used for copyright protection, copy control and data mining for digital videos on the Internet.

The invention provides a video copy detection method and a video copy detection system based on a soft cascade model sensitive to deformation. The method comprises the following steps of: preprocessing, namely extracting vision key frames and audio frames of which the formats are consistent from an inquiry video; identifying deformation, namely distinguishing the type of the deformation of the inquiry video, and transmitting the type information to a cascade detector chain corresponding to the type; and detecting, namely sequentially processing the inquiry video by detectors in the cascade detector chain until a certain detector determines that the inquiry video is a copy or all the detectors determine that the inquiry video is not a copy, wherein in a certain detector, the vision key frames or the audio frames of the inquiry video are searched according to a vision characteristic or an audio characteristic; and then a time domain pyramid trusted platform module (TPM) is used for integrating search results of the frame layers into a copy detection result of a video layer. According to the method and the system, whether the inquiry video is a copy of a given reference video base can be accurately and quickly distinguished; and the method and the system have great significance in application to the fields of digital copyright management, advertisement tracking, video content filtration and the like.

A method for a matching technique for detecting copies of a video clip is provided. The method includes comparing temporal trails of intensity distributions between frames of the video clip and frames of a target video. A computer readable media, a computer system and integrated circuit for video copy detection are also provided.

The invention belongs to the technical field of video images, in particular to a video copying detection method being suitable for IPTV, comprising the following steps: detecting 'picture in picture' of a video; dividing and gathering the video lens; calculating video frame similarity; and constructing video lens index by utilizing a K-means tree and the like. The method of the invention realizes rapid and efficient video copying detection by utilizing a concise and effective video frame similarity processing method and utilizing a K-means tree to construct lens index; and the method for performing the lens detection is not only rapid, but also has good detection accuracy and coverage rate.

The invention is applicable to the technical field of multimedia information processing, and provides a video copy detection method and system thereof. The method comprises the following steps: obtaining an image tag Y in the image training set; carrying out copy attack on each image tag Y to obtain a first image X. The training model of depth feature extraction model is constructed, and the depthfeature extraction model is obtained by training. Obtaining a first fusion feature after fusing the first depth feature and the first DCT coefficient feature of each second image, and storing the first fusion feature as a fusion feature database; Extracting a third key frame of the query video, and fusing the second depth feature and the second DCT coefficient feature to obtain a second fusion feature; Each of the second fusion features is matched with each of the first fusion features to obtain a detection result of the query video. Thus, the invention improves the discrimination and robustness of the video features and improves the accuracy of the video copy detection system.

The invention belongs to the technical field of computer video processing and particularly discloses a video copying detection method for advertisement detection. The video copying detection method for the advertisement detection comprises firstly detecting candidate matching advertisement fragments through an audio matching method, then verifying the candidate matching advertisement fragments through an image matching method and achieving video copying detection. The video copying detection method for the advertisement detection has the advantages of being efficient due to an algorithm which combines image and audio features and enabling large-sized video to be processed and being rapid in detection process and accurate in result.

The invention provides a video copy detection method based on improved OM features. The method comprises the steps that the improved OM features are extracted from a source video data set, and an index is established; the improved OM features are extracted from a query video, a source video most similar to the query video is calculated based on the index, and when the similarity value is larger than a set value, the source video is selected as a candidate source video; the number of sequence matching key frames between the candidate source video and the query video is calculated, whether the query video is a copy of the candidate source video or not is judged according to a threshold value, and the copied segment in the source video is positioned. According to the method, the spatial feature of a video frame image and the brightness change between video frames are taken into full consideration, and the spatial feature and the brightness change promote each other, so that video content is described more accurately, and consequently a higher accuracy rate of video copy detection is obtained.

The invention discloses a video copy detection system based on a convolutional and recurrent neural network. The system includes five modules, which are a data set establishment module, a frame feature extraction module, a spatio-temporal feature training module, a loop network test module and a copy video matching module. The spatio-temporal feature training module also includes video editing module and loop network training module. The invention adopts a residual convolutional neural network to extract deeper frame-level feature representationto improve the detection accuracy and reduce thedetection recall effectively. A twin-loop neural network is used to fuse multiple frame-level features, and the spatio-temporal feature representation is generated by using the dynamic information between frames, so the spatio-temporal fusion between sequences is realized, and video matching takes less time and occupies less memory.

The invention relates to an audio / video (AV) copy detection method and device. The AV copy detection method includes: acquiring an AV image, performing decoding and pretreatment on the AV image, and performing feature extraction on the acquired audio part and the acquired video frames to obtain corresponding audio features and corresponding image features of the video frames; fusing the audio features and the image features of the video frames corresponding to the AV image to obtain AV fusion features; matching the AV fusion features based on a preset feature library of a reference video to obtain a frame set matching result; and performing copy determination and positioning on the AV image based on the frame set matching result and the reference video. Through the method for fusing the audio and the video, the robustness of the video copy detection system can be enhanced; the audio features and the video features are fused, the execution efficiency of the copy detection system can be greatly improved; and the audio and the video are analyzed at the same time, and then the positioning accuracy of copy segments can be improved.

A method for a matching technique for detecting copies of a video clip is provided. The method includes comparing temporal trails of intensity distributions between frames of the video clip and frames of a target video. A computer readable media, a computer system and integrated circuit for video copy detection are also provided.

Content streams may be segmented to provide automatic extraction and storage of content items without intervening commercials or other unrelated content. These content items may then be stored in a database and made accessible to subscribers through, for example, an on-demand service. Automatic segmentation may include the identification of program boundaries, segmentation of a content stream based on the boundaries and the subsequent classification of the segments into content types. For example, audio and video duplication detection may be used to identify commercials since commercials tend to repeat frequently over a relatively short amount of time. A system may further identify an end of program indicator in a video stream to determine when a program ends. Accordingly, if a program ends after a scheduled end time, a recording device (e.g., the program is being recorded) may automatically extend the recording time to capture the entire program.

The invention discloses a method and system for identifying harmful videos. The method comprises the steps of when it is judged that the page elements of a webpage comprise the URL path of the video,identifying an IP address or an IP address field of a user recorded in the page content of the webpage, acquiring a domain name contained in the URL or an IP address pointed by the URL according to the URL path of a video, and outputting a first weight factor and a second weight factor based on the related query of the IP address and the domain name; obtaining a video file at the lowest picture quality, and carrying out the video copy detection on the video file of the lowest picture quality in a pre-established harmful video database, and outputting a third weight factor according to a monitoring result; and combining the first weight factor, the second weight factor and the third weight factor to identify whether the video belongs to the harmful video. According to the method and the system, by combining a database made by the big data, using the image processing means as few as possible and utilizing multiple modes, a scheme for identifying the harmful videos is provided.

The invention belongs to the technical field of the video processing, and particularly relates to a method for identifying content of a digital video, which mainly comprises a key frame extraction module, a video copy detection module and a video frame content identification module. The video copy detection module adopts a video copy detection method based on local ordering to ensure that a similar frame is rapidly obtained through a given possibility, and to improve the detection performance. The video frame content identification module adopts a pornographic image identification algorithm, the algorithm comprises four weak classifiers (skin area proportion classifier, face quantity classifier, invariant-moment classifier and skin area distribution classifier), the four weak classifiers are integrated to form one strong classifier, the result of the strong classifier is the final detection result, so the mistake recognition rate and nonrecognition rate can be effectively reduced. Themethod has wide application value for the internet digital video information monitoring.