A number of automated video shot boundary detection methods for indexing a videosequence to facilitate browsing and retrieval have been proposed in recent years.Among these methods,the dissolve shot boundary isn't...A number of automated video shot boundary detection methods for indexing a videosequence to facilitate browsing and retrieval have been proposed in recent years.Among these methods,the dissolve shot boundary isn't accurately detected because it involves the camera operation and objectmovement.In this paper,a method based on support vector machine (SVM) is proposed to detect thedissolve shot boundary in MPEG compressed sequence.The problem of detection between the dissolveshot boundary and other boundaries is considered as two-class classification in our method.Featuresfrom the compressed sequences are directly extracted without decoding them,and the optimal classboundary between two classes are learned from training data by using SVM.Experiments,whichcompare various classification methods,show that using proposed method encourages performance ofvideo shot boundary detection.展开更多
A shot presents a contiguous action recorded by an uninterrupted camera operation and frames within a shot keep spatio-temporal coherence. Segmenting a serial video stream file into meaningful shots is the first pass ...A shot presents a contiguous action recorded by an uninterrupted camera operation and frames within a shot keep spatio-temporal coherence. Segmenting a serial video stream file into meaningful shots is the first pass for the task of video analysis, content-based video understanding. In this paper, a novel scheme based on improved two-dimensional entropy is proposed to complete the partition of video shots. Firstly, shot transition candidates are detected using a two-pass algorithm： a coarse searching pass and a fine searching pass. Secondly, with the character of two-dimensional entropy of the image, correctly detected transition candidates are further classified into different transition types whereas those falsely detected shot breaks are distinguished and removed. Finally, the boundary of gradual transition can be precisely located by merging the characters of two-dimensional entropy of the image into the gradual transition. A large number of video sequences are used to test our system performance and promising results are obtained.展开更多
In the present technological world,surveillance cameras generate an immense amount of video data from various sources,making its scrutiny tough for computer vision specialists.It is difficult to search for anomalous e...In the present technological world,surveillance cameras generate an immense amount of video data from various sources,making its scrutiny tough for computer vision specialists.It is difficult to search for anomalous events manually in thesemassive video records since they happen infrequently and with a low probability in real-world monitoring systems.Therefore,intelligent surveillance is a requirement of the modern day,as it enables the automatic identification of normal and aberrant behavior using artificial intelligence and computer vision technologies.In this article,we introduce an efficient Attention-based deep-learning approach for anomaly detection in surveillance video(ADSV).At the input of the ADSV,a shots boundary detection technique is used to segment prominent frames.Next,The Lightweight ConvolutionNeuralNetwork(LWCNN)model receives the segmented frames to extract spatial and temporal information from the intermediate layer.Following that,spatial and temporal features are learned using Long Short-Term Memory(LSTM)cells and Attention Network from a series of frames for each anomalous activity in a sample.To detect motion and action,the LWCNN received chronologically sorted frames.Finally,the anomaly activity in the video is identified using the proposed trained ADSV model.Extensive experiments are conducted on complex and challenging benchmark datasets.In addition,the experimental results have been compared to state-ofthe-artmethodologies,and a significant improvement is attained,demonstrating the efficiency of our ADSV method.展开更多
It is difficult to detect dissolve accurately in video segmentation. Two new parameters AEI and IDM are computed to describe dissolve. An improved method based on the change curves of AEI and IDM is proposed to detect...It is difficult to detect dissolve accurately in video segmentation. Two new parameters AEI and IDM are computed to describe dissolve. An improved method based on the change curves of AEI and IDM is proposed to detect dissolve accurately. The experiments show that this method can detect dissolve accurately.展开更多
文摘A number of automated video shot boundary detection methods for indexing a videosequence to facilitate browsing and retrieval have been proposed in recent years.Among these methods,the dissolve shot boundary isn't accurately detected because it involves the camera operation and objectmovement.In this paper,a method based on support vector machine (SVM) is proposed to detect thedissolve shot boundary in MPEG compressed sequence.The problem of detection between the dissolveshot boundary and other boundaries is considered as two-class classification in our method.Featuresfrom the compressed sequences are directly extracted without decoding them,and the optimal classboundary between two classes are learned from training data by using SVM.Experiments,whichcompare various classification methods,show that using proposed method encourages performance ofvideo shot boundary detection.
基金Supported by the National Natural Science Foundation of China (Grant No.60675017)National Basic Research Program of China (Grant No.2006CB303103)
文摘A shot presents a contiguous action recorded by an uninterrupted camera operation and frames within a shot keep spatio-temporal coherence. Segmenting a serial video stream file into meaningful shots is the first pass for the task of video analysis, content-based video understanding. In this paper, a novel scheme based on improved two-dimensional entropy is proposed to complete the partition of video shots. Firstly, shot transition candidates are detected using a two-pass algorithm： a coarse searching pass and a fine searching pass. Secondly, with the character of two-dimensional entropy of the image, correctly detected transition candidates are further classified into different transition types whereas those falsely detected shot breaks are distinguished and removed. Finally, the boundary of gradual transition can be precisely located by merging the characters of two-dimensional entropy of the image into the gradual transition. A large number of video sequences are used to test our system performance and promising results are obtained.
基金This research was supported by the Chung-Ang University Research Scholarship Grants in 2021 and the Culture,Sports and Tourism R&D Program through the Korea Creative Content Agency grant funded by the Ministry of Culture,Sports,and Tourism in 2022(Project Name:Development of Digital Quarantine and Operation Technologies for Creation of Safe Viewing Environment in Cultural Facilities,Project Number:R2021040028,Contribution Rate:100%).
文摘In the present technological world,surveillance cameras generate an immense amount of video data from various sources,making its scrutiny tough for computer vision specialists.It is difficult to search for anomalous events manually in thesemassive video records since they happen infrequently and with a low probability in real-world monitoring systems.Therefore,intelligent surveillance is a requirement of the modern day,as it enables the automatic identification of normal and aberrant behavior using artificial intelligence and computer vision technologies.In this article,we introduce an efficient Attention-based deep-learning approach for anomaly detection in surveillance video(ADSV).At the input of the ADSV,a shots boundary detection technique is used to segment prominent frames.Next,The Lightweight ConvolutionNeuralNetwork(LWCNN)model receives the segmented frames to extract spatial and temporal information from the intermediate layer.Following that,spatial and temporal features are learned using Long Short-Term Memory(LSTM)cells and Attention Network from a series of frames for each anomalous activity in a sample.To detect motion and action,the LWCNN received chronologically sorted frames.Finally,the anomaly activity in the video is identified using the proposed trained ADSV model.Extensive experiments are conducted on complex and challenging benchmark datasets.In addition,the experimental results have been compared to state-ofthe-artmethodologies,and a significant improvement is attained,demonstrating the efficiency of our ADSV method.
文摘It is difficult to detect dissolve accurately in video segmentation. Two new parameters AEI and IDM are computed to describe dissolve. An improved method based on the change curves of AEI and IDM is proposed to detect dissolve accurately. The experiments show that this method can detect dissolve accurately.
