Shot Boundary Detection Algorithm Research Articles

Video Shot Boundary Detection and Sports Video Classification Algorithm Based on Particle Filter

INTRODUCTION: Sports video is a very important information resource. The classification of sports video with high accuracy can effectively improve the browsing and query effect of users. This project intends to study a motion video classification algorithm based on deep learning particle filter to solve the problems of strong subjectivity and low accuracy of existing motion video classification algorithms. A key box extraction method based on similarity is proposed. The moving video classification algorithm based on deep learning coding model is studied. Examples of various types of sports videos are analyzed. The overall performance of the motion video classification algorithm proposed in this paper is much better than other existing motion video classification algorithms. This algorithm can greatly improve the classification performance of motion video.

Shot Boundary Detection Using Color Clustering and Attention Mechanism

Shot boundary detection (SBD) is widely used in scene segmentation, semantic analysis, and video retrieval. However, existing SBD algorithms have certain applications in video processing, but they have the following three problems. First, these algorithms cannot effectively handle shot boundaries caused by sudden lighting changes. Second, when there are dimly lighting frames in the video, these algorithms cannot perform boundary detection well. Third, when there is object or camera motion in the video, these algorithms also fail to work. To resolve these issues, we propose an SBD algorithm with color clustering changes in small regions (CCSR) to detect the shot transitions, which are abrupt changes and gradual transitions (dissolve and fade). The main idea behind the CCSR algorithm is to compute the distance of color features and to preserve the spatio-temporal information as much as possible. This model has relatively less dependence on the threshold parameters and sliding windows. Unlike other SBD algorithms, the clustering results of CCSR weaken factors such as object motion and illumination changes between adjacent frames in the video, which is helpful for reducing false detections. Furthermore, we utilize an attention mechanism in the gradual transitions to improve detection efficiency and accuracy. Finally, we evaluated the SBD algorithm, which was tested on a standard TRECVID dataset. The experimental results suggest that our algorithm yields significant improvements in precision and recall compared to the current techniques, with an average improvement of 10.35% and 8.85%, respectively. Moreover, compared with state-of-the-art algorithms, the results prove that the proposed method improves the F-score by more than 2.64% and the computation time efficiency by over 10%.

Shot Boundary Detection Based on Global Features and the Target Features

Video processing plays an important role in the intelligent monitoring and management system of agricultural information. Video shot boundary detection is the basic symmetry step underlying video processing techniques. According to the current shot boundary detection algorithm, the feature changes between gradual transition frames are difficult to detect, and the misdetection situation is caused by ignoring the attention of the target feature during the feature extraction. A novel symmetry multi-step comparative scheme of shot boundary detection algorithm based on global features and target features is proposed. First, the RGB color histogram features of the video frame are extracted. Second, foreground object detection for the video frames is performed using the Gaussian Mixture Model (GMM), and the scale-invariant features transformation (SIFT) of the foreground targets is extracted. Finally, global features and target features fusion through weights, calculating the difference between adjacent frames across multiple steps, generate a pattern distance map. The pattern distance map of the gradual transition and the cut detection is different; we can judge the gradual transition and the cut detection according to the pattern distance map. Experiments show that the proposed symmetry method improves by about 2% in recall and accuracy compared to other algorithms.

EADN: An Efficient Deep Learning Model for Anomaly Detection in Videos

Surveillance systems regularly create massive video data in the modern technological era, making their analysis challenging for security specialists. Finding anomalous activities manually in these enormous video recordings is a tedious task, as they infrequently occur in the real world. We proposed a minimal complex deep learning-based model named EADN for anomaly detection that can operate in a surveillance system. At the model’s input, the video is segmented into salient shots using a shot boundary detection algorithm. Next, the selected sequence of frames is given to a Convolutional Neural Network (CNN) that consists of time-distributed 2D layers for extracting salient spatiotemporal features. The extracted features are enriched with valuable information that is very helpful in capturing abnormal events. Lastly, Long Short-Term Memory (LSTM) cells are employed to learn spatiotemporal features from a sequence of frames per sample of each abnormal event for anomaly detection. Comprehensive experiments are performed on benchmark datasets. Additionally, the quantitative results are compared with state-of-the-art methods, and a substantial improvement is achieved, showing our model’s effectiveness.

V2T: video to text framework using a novel automatic shot boundary detection algorithm

V2T: video to text framework using a novel automatic shot boundary detection algorithm

A novel bifold-stage shot boundary detection algorithm: invariant to motion and illumination

Shot boundary detection is mainly considered as a stepping stone in the broad arena of content-based video retrieval. Ample systematic investigation has been carried out in the terrain of shot boundary detection. The attainment of shot boundary detection procedures is greatly hindered due to the presence of unforeseen illumination change and motion effects in a video. This work proposes a novel bifold-stage technique to recognize abrupt transition in videos, invariant to motion, and illumination effects. In the first stage, the local ternary patterns feature is used to extract information from each frame in a video. Then, a set of novel adaptive thresholds such as $${\gamma }$$ and $${{{\beta }}}$$ are used to find the possible transition frames. In the confirmation stage, Lab color difference along with an adaptive threshold $${\delta }$$ is used to extract actual transition frames. The experimental result depicts that the motion effect is also scaled down in the initial stage. The Lab color difference passed down in the second stage also handles the illumination and motion effects which are not managed in the initial stage. Experimentation is done using TRECVid 2001 and 2007 standard datasets and palpable that the proposed technique outperforms most of the contemporary shot boundary detection approaches.

Fast Shot Boundary Detection Based on Separable Moments and Support Vector Machine

The large number of visual applications in multimedia sharing websites and social networks contribute to the increasing amounts of multimedia data in cyberspace. Video data is a rich source of information and considered the most demanding in terms of storage space. With the huge development of digital video production, video management becomes a challenging task. Video content analysis (VCA) aims to provide big data solutions by automating the video management. To this end, shot boundary detection (SBD) is considered an essential step in VCA. It aims to partition the video sequence into shots by detecting shot transitions. High computational cost in transition detection is considered a bottleneck for real-time applications. Thus, in this paper, a balance between detection accuracy and speed for SBD is addressed by presenting a new method for fast video processing. The proposed SBD framework is based on the concept of candidate segment selection with frame active area and separable moments. First, for each frame, the active area is selected such that only the informative content is considered. This leads to a reduction in the computational cost and disturbance factors. Second, for each active area, the moments are computed using orthogonal polynomials. Then, an adaptive threshold and inequality criteria are used to eliminate most of the non-transition frames and preserve candidate segments. For further elimination, two rounds of bisection comparisons are applied. As a result, the computational cost is reduced in the subsequent stages. Finally, machine learning statistics based on the support vector machine is implemented to detect the cut transitions. The enhancement of the proposed fast video processing method over existing methods in terms of computational complexity and accuracy is verified. The average improvements in terms of frame percentage and transition accuracy percentage are 1.63% and 2.05%, respectively. Moreover, for the proposed SBD algorithm, a comparative study is performed with state-of-the-art algorithms. The comparison results confirm the superiority of the proposed algorithm in computation time with improvement of over 38%.

Eagle Eye CBVR Based on Unique Key Frame Extraction and Deep Belief Neural Network

Great efforts have been paid to develop Content Based Video Retrieval (CBVR) due to the exponential growth of video datasets with several types of data such as visual, audio and metadata. This paper proposes Eagle Eye a new Content Based Video Retrieval Framework through applying new methods for the major three important parts of CBVR. A Subspace Clustering Algorithm is implemented with binary codes that are extracted from different videos. We introduce a Local Histogram based shot boundary detection algorithm to detect shot boundaries. Then Unique Key Frame Summarization algorithm with detected shots is applied. An integrated Local Binary Pattern, Coherence based filter and HOG algorithm is used for the Feature extraction and best features are selected and stored along with the video indices. The extracted features were then utilized to train a classifier. A Deep Belief Neural Network classifier trains each extracted feature of query frame and matches the best features of the query frames with other video features. Then we enhance the video retrieval by introducing video hashing method, combining low level and high-level semantic features for the elimination of repetitive video in the retrieval. We used precision, accuracy, sensitivity and specificity metrics to assess the applicability of the projected technique. Xiph.org and Youtube datasets are used for the valuation analysis. The experimental results show that the Eagle Eye provides better performance and less processing time compared to the other methods.

Video Shot Boundary Detection Based on Feature Fusion and Clustering Technique

For the problems of low accuracy and high complexity in detection of gradual shot boundary and long shot, a new video shot boundary detection algorithm based on feature fusion and clustering technique (FFCT) is proposed. In the algorithm, the interval frames of video sequence are selected, converted to gray images and scaled by sampling. With the frames, the speed-up robust features (SURF) and fingerprint features are extracted from non-compressed domain and compressed domain, and then the extracted features are fused. Next, K-means method is used to cluster the fused features, and linear discriminant analysis (LDA) is introduced to map the clusters to realize cohesion within classes and looseness among classes. Finally, the correlation of the feature classes between frames is calculated, and the features in each class are selected through density calculation and matched to realize the coarse detection and fine detection of video shot boundary. In the experiment, compared with the latest representative algorithms, it has the highest accuracy for the proposed algorithm. In particular, the detection of gradual shot boundary and long shot are also more accurate. Meanwhile, the average time consumption is also reduced. The experimental results show that the proposed algorithm has high accuracy and time efficiency, especially for gradual shot boundary and long shot detection.

A novel automatic shot boundary detection algorithm: robust to illumination and motion effect

Many researches have been done on shot boundary detection, but the performance of shot boundary detection approaches is yet to be addressed for the videos having sudden illumination and object/camera motion effects efficiently. In this paper, a novel dual-stage approach for an abrupt transition detection is proposed which is able to withstand under certain illumination and motion effects. Firstly, an adaptive Wiener filter is applied to the lightness component of the frame to retain some important information on both frequencies and LBP-HF is extracted to reduce the illumination effect. From the experimentation, it is also confirmed that the motion effect is also reduced in the first stage. Secondly, Canny edge difference is used to further remove the illumination and motion effects which are not handled in the first stage. TRECVid 2001 and TRECVid 2007 datasets are applied to analyze and validate our proposed algorithm. Experimental results manifest that the proposed system outperforms the state-of-the-art shot boundary detection techniques.

A novel video shot boundary detection framework employing DCT and pattern matching

The video Shot Boundary Detection (SBD) is an elementary step in realising a system capability to perform content based video search, structural analysis, data retrieval and video summation. Myriad research works in the past have been reported to construct SBD algorithms. However, the need of an error-free, meticulous and cost-effective SBD technique still persists; for applications viz. apt management, storage, browsing, video indexing and retrieval of multimedia data. This paper is an effort in the same direction with the aim of achieving high execution speed and greater accuracy. The proposed SBD technique in this paper incorporates three steps: (i) Candidate Segment Selection (ii) Cut Transition detection (iii) Gradual Transition detection. This paper adopts pixel based technique with candidate segment selection to speed up the SBD. For Cut Transition detection, the proposed method employs Discrete Cosine Transform (DCT) and for Gradual Transition detection, it employs Image Histogram and Pattern Matching. The comparison of MATLAB simulation results of the proposed SBD technique with those in literature manifest better results in terms of execution speed and accuracy.

A Proposed Algorithm for Shot Boundary Detection Using Various Features

A Proposed Algorithm for Shot Boundary Detection Using Various Features

Shot boundary detection based on orthogonal polynomial

Shot boundary detection (SBD) is a substantial step in video content analysis, indexing, retrieval, and summarization. SBD is the process of automatically partitioning video into its basic units, known as shots, through detecting transitions between shots. The design of SBD algorithms developed from simple feature comparison to rigorous probabilistic and using of complex models. Nevertheless, accelerate the detection of transitions with higher accuracy need to be improved. Extensive research has employed orthogonal polynomial (OP) and their moments in computer vision and signal processing owing to their powerful performance in analyzing signals. A new SBD algorithm based on OP has been proposed in this paper. The Features are derived from orthogonal transform domain (moments) to detect the hard transitions in video sequences. Moments are used because of their ability to represent signal (video frame) without information redundancy. These features are the moments of smoothed and gradients of video frames. The moments are computed using a developed OP which is squared Krawtchouk-Tchebichef polynomial. These moments (smoothed and gradients) are fused to form a feature vector. Finally, the support vector machine is utilized to detect hard transitions. In addition, a comparison between the proposed algorithm and other state-of-the-art algorithms is performed to reinforce the capability of the proposed work. The proposed algorithm is examined using three well-known datasets which are TRECVID2005, TRECVID2006, and TRECVID2007. The outcomes of the comparative analysis show the superior performance of the proposed algorithm against other existing algorithms.

Curvelet transform based feature extraction and selection for multimedia event classification

Multimedia event classification has been one of the major endeavors in video event analysis. For event identification, feature extraction plays a critical role, merely distinguishing the right features becomes a challenging job. So, in this paper, feature extraction and selection for video event detection are proposed. For video event detection or classification, identification of object structure and its motion are a basic needs. So, for object detection, curvelet features are considered, due to its high directional selectivity and high anisotropic properties. Next, an algorithm for Shot Boundary Detection (SBD) called Motion based SBD (MSBD) is proposed to identify the shot boundaries. Also, to make the users to access the queried event in less time, an algorithm for representing few representative frames containing whole video content is proposed. To make an event search efficient, object-based features from dominant features are extracted and to enhance the features much more efficient and dominant, feature selection is performed using the ranking method. Lastly, an SVM classifier with RBF kernel is used for event classification. The proposed work is experimented using Columbia Consumer Video (CCV) dataset and evaluated using mean Average Precision (mAP) and find that, it outperforms various other existing methods.

IMPULSIVE INTERMODAL CYBER BULLYING RECOGNITION FROM PUBLIC NETS

Cyberbullying has grown as an important societal challenge nowadays. The Cyberbullying affects both in terms of psychological and emotional means of a person. So there is a need to devise a method to detect and prevent cyberbullying in social networks. Most of the existing cyberbullying methods involves only text detection and few methods are available for analysing the visual detection. In this proposed work is going to detect multimodel cyberbullying such as audio, video, image along with text in the social networks. The cyberbully image will be detected using the computer vision algorithm which includes two methods like Image Similarity and Optical Character Recognition (OCR). The cyberbully video will be detected using the Shot Boundary detection algorithm where the video will be broken into frames and analysed using various methods in it. The proposed framework also support to identify the cyberbully audio in the social network. Finally the cyberbully data will be classified into Physical bullying, Social bullying and Verbal bullying using classifiers.

Video Shot Boundary Detection using the Scale Invariant Feature Transform and RGB Color Channels

<p>Segmentation of the video sequence by detecting shot changes is essential for video analysis, indexing and retrieval. In this context, a shot boundary detection algorithm is proposed in this paper based on the scale invariant feature transform (SIFT). The first step of our method consists on a top down search scheme to detect the locations of transitions by comparing the ratio of matched features extracted via SIFT for every RGB channel of video frames. The overview step provides the locations of boundaries. Secondly, a moving average calculation is performed to determine the type of transition. The proposed method can be used for detecting gradual transitions and abrupt changes without requiring any training of the video content in advance. Experiments have been conducted on a multi type video database and show that this algorithm achieves well performances.</p>

Detecting shot boundary with sparse coding for video summarization

Keyframe selection is a common way to summarize video contents. However, delimiting shot boundaries to extract a representative keyframe from each shot is not trivial as most shot boundary techniques are heuristic and sensitive to the types of video transitions. This paper proposes a new shot boundary detection algorithm, that learns a dictionary from the given video using sparse coding and updates atoms in the dictionary, following the philosophy that different shots cannot be reconstructed using the learned dictionary. Technically, our algorithm conducts the learning by simultaneously minimizing the reconstruction loss, restricting the sparsity of the reconstruction matrix, and preserving the structure across patches and frames. Once shot boundaries are determined, one representative keyframe is selected from each shot and then a video summary is constructed by concatenating the representative keyframes through a post process. On two standard video datasets across various genres, i.e., VSUMM and YouTube datasets, our method is shown to be powerful for video summarization with superior performance over several state-of-the-art techniques.

Review of Research on Shot Boundary Detection Algorithm of the Compressed Video Domain in Content-based Video Retrieval Technique

Based on the background of extensive use of the video retrieval technique in substation monitoring, this paper focuses on the research of the shot boundary detection methods in the video retrieval technique. The research content is mainly involved in the following four kinds of shot boundary detection methods: the shot boundary detection method based on the base tone, the shot boundary detection method based on the macro- block motion information, the shot boundary detection method based on the motion vector and the video shot segmentation method based on clustering. Through the in-depth explanation and comparison of four kinds of shot boundary detection methods, this paper systematically summarizes the characteristics of four kinds of shot boundary detection methods, and also vividly points out the problems of four kinds of shot boundary detection methods, thus pointing out the direction for the future development of video retrieval technique.

Automatic Multi Model Cyber Bullying Detection from Social Networks

Cyberbullying has grown as an important societal challenge nowadays. The Cyberbullying affects both in terms of psychological and emotional means of a person. So there is a need to devise a method to detect and prevent cyberbullying in social networks. Most of the existing cyberbullying methods involves only text detection and few methods are available for analysing the visual detection. In this proposed work is going to detect multimodel cyberbullying such as audio, video, image along with text in the social networks. The cyberbully image will be detected using the computer vision algorithm which includes two methods like Image Similarity and Optical Character Recognition (OCR). The cyberbully video will be detected using the Shot Boundary detection algorithm where the video will be broken into frames and analysed using various methods in it. The proposed framework also support to identify the cyberbully audio in the social network. Finally the cyberbully data will be classified into Physical bullying, Social bullying and Verbal bullying using classifiers.

Comparative analysis of shot boundary detection algorithms for video summarization

Video summarization is very effective process in order to extract the essential and necessary information from huge videos and convert them into summarized videos. In this paper, we study and evaluate the comparative analysis of shot boundary detection algorithms of video summarization. The histogram based and edge based algorithms already exists are studied and compared with the new proposed improved histogram algorithm. The experimental evaluation validates the proposed approach returning most representative keyframes. The graphical representation of keyframes through above mentioned algorithms makes it clearer that new proposed algorithm delivers better and respectable results.