A novel automatic shot boundary detection algorithm: robust to illumination and motion effect (original) (raw)
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International Journal of Computer Applications, 2012
Video Shot boundary detection is the process of automatically detecting the boundaries between shots in video. Shot Boundary detection is an important fundamental process in video data access, indexing, search and retrieval. The increased availability and usage of on-line digital video has created a need for automated video content analysis techniques. Detection of gradual transition and elimination of disturbances caused by illumination change is a major challenge in shot boundary detection technique. These disturbances are often mistaken as shot boundaries. It is a crucial task to develop a method that is not only insensitive to illumination change but also sensitive to detect shot change. An algorithm is proposed for shot boundary detection in presence of illumination change. This is very important for accurate detection of shot boundaries and very useful in content based analysis of video. First the algorithm removes illumination change using discrete cosine transform and discrete wavelet transform. Then shot boundaries are detected using normal difference & wavelet difference. A shot boundary is detected when the feature difference shows sharp change greater than threshold. Experimental study is performed on number of videos that include significant illumination change. The performance of proposed algorithm is better as compared to existing techniques
Shot boundary detection in the presence of illumination and motion
Signal, Image and Video Processing, 2011
Detection of gradual transition and the elimination of disturbances caused by illumination change or fast object and camera motion are the major challenges to the current shot boundary detection techniques. These disturbances are often mistaken as shot boundaries. Therefore, it is a challenging task to develop a method that is not only insensitive to various disturbances but also sensitive enough to capture a shot change. To address these challenges, we propose an algorithm for shot boundary detection in the presence of illumination change, fast object motion, and fast camera motion. This is important for accurate and robust detection of shot boundaries and in turn critical for high-level contentbased analysis of video. First, the propose algorithm extracts structure features from each video frame by using dual-tree complex wavelet transform. Then, spatial domain structure similarity is computed between adjacent frames. The declaration of shot boundaries are decided based on carefully chosen thresholds. Experimental study is performed on a number of videos that include significant illumination change and fast motion of camera and objects. The performance comparison of the proposed algorithm with other existing techniques validates its effectiveness in terms of better Recall, Precision, and F1 score.
Video Shot Boundary Detection Algorithm
Lecture Notes in Computer Science, 2006
We present a newly developed algorithm for automatically segmenting videos into basic shot units. A basic shot unit can be understood as an unbroken sequence of frames taken from one camera. At first we calculate the frame difference by using the local histogram comparison, and then we dynamically scale the frame difference by Log-formula to compress and enhance the frame difference. Finally we detect the shot boundaries by the newly proposed shot boundary detection algorithm which it is more robust to camera or object motion, and many flashlight events. The proposed algorithms are tested on the various video types and experimental results show that the proposed algorithm are effective and reliably detects shot boundaries.
Comparison of automatic shot boundary detection algorithms
Storage and Retrieval for Image and Video Databases VII, 1998
variations including color histogram, edge directions histogram and wavelet transformations statistics. The performance and ease of selecting good thresholds for these algorithms are evaluated based on a wide variety of video sequences with different object and camera motions. Threshold selection is performed using sliding window. We used TV news, sports and documentary, music, movie and nature video sequences to estimate the performance of the algorithms. The experimental results indicate that the algorithm based on color histograms is most suitable for shot boundary detection in film and documentary categories, but the algorithm based on wavelet is preferable for nature and sports categories.
—Video shot boundary detection (SBD), is the process of segmenting a video sequence into smaller temporal units, dubbed shots. SBD is the primary step for any further video analyses. In this article we propose a new and effective SBD method for detecting cut transitions (CTs) and gradual transitions (GTs) of a video sequence, in a unified scheme. As the first step in our scheme, we introduce a significantly effective candidate segment selection method by use of frame histogram, and a locally-defined adaptive threshold. This step avoids the processing of unnecessary video segments in the subsequent stages and accelerate the whole SBD process. Next, we extract CTs from candidate segments by use of a robust multi-stage thresholding. Then, we pass the remained segments through a novel candidate segment adjustment stage, which is realized by use of K-means classifiers, and its purpose is to give a better initial prediction of the location of GTs. Afterwards, we use some measures of difference along with singular value decomposition (SVD) on histograms of frames in each candidate GT segment, to obtain discriminating feature vectors with desired dimensions. Features are then fed into linear support vector machine (SVM) classifiers to separate segments with GTs from the rest. Finally, we determine the boundary of GTs by evaluating the gradient of a metric associated to each GT segment. By use of gradient for determining the range of GTs, we avoid the iterative procedures that are usually used in this purpose. Experimental results on standard videos, show the capability and supremacy of our method in comparison with state-of-the-art SBD procedures.
Video Shot Boundary Detection: A Review
Advances in Intelligent Systems and Computing, 2015
Video image processing is a technique to handle the video data in an effective and efficient way. It is one of the most popular aspects in the video and image based technologies such as surveillance. Shot change boundary detection is also one of the major research areas in video signal processing. Previous works have developed various algorithms in this domain. In this paper, a brief literature survey is presented that establishes an overview of the works that has been done previously. In this paper we have discussed few algorithms that were proposed previously which also includes histogram based, DCT based and motion vector based algorithms as well as their advantages and their limitations.
Shot boundary detection using frame transition parameters and edge strength scatter
2007
We have presented a unified model for various types of video shot transitions. Based on that model, we adhere to frame estimation scheme using previous and next frames. The frame parameters accompanied by a scatter measure of edge strength and average intensity constitute the feature vector of a frame. Finally, the frames are classified as no change (within shot frame), abrupt change or gradual change frames using a multilayer perceptron network. The scheme is free from the problems of selecting thresholds and/or window size as used by various schemes. Moreover, the handling of both, abrupt and gradual transitions along with non-transition frames under a single and uniform framework is the unique feature of the work.
Video Shot Boundary Detection Using Various Techniques
2015
Shot and classification is first and foremost step for further analysis of video content. A Shot is defined as a set of frames from a single camera. Processing of video and image facilitates better understanding of the scene that it describe. It is a fundamental component of a number of technologies like video surveillance, robotics etc .A scene is a collection of one or more shots focusing on one or more object of interest. various shot boundary methods have been developed which can detect cut and gradual shot simultaneously.
Comparison of Automatic Shot Boundary Detection Algorithms Based On Color, Edges and Wavelets
Shot boundary detection is fundamental to video analysis since it segments a video into its basic components. This paper presents a comparison of several shot boundary detection techniques and their variations including color histogram, edge directions histogram and wavelet transformations statistics. The performance and ease of selecting good thresholds for these algorithms are evaluated based on a wide variety of video sequences with different object and camera motions. Threshold selection is performed using sliding window. We used TV news, sports and documentary, music, movie and nature video sequences to estimate the performance of the algorithms. The experimental results indicate that the algorithm based on color histograms is most suitable for shot boundary detection in film and documentary categories, but the algorithm based on wavelet is preferable for nature and sports categories.
A perceptual scheme for fully automatic video shot boundary detection
Signal Processing: Image Communication, 2014
In this paper, we propose a novel and robust modus operandi for fast and accurate shot boundary detection where the whole design philosophy is based on human perceptual rules and the well-known "Information Seeking Mantra". By adopting a top-down approach, redundant video processing is avoided and furthermore elegant shot boundary detection accuracy is obtained under significantly low computational costs. Objects within shots are detected via local image features and used for revealing visual discontinuities among shots. The proposed method can be used for detecting all types of gradual transitions as well as abrupt changes. Another important feature is that the proposed method is fully generic, which can be applied to any video content without requiring any training or tuning in advance. Furthermore, it allows a user interaction to direct the SBD process to the user 0 s "Region of Interest" or to stop it once satisfactory results are obtained. Experimental results demonstrate that the proposed algorithm achieves superior computational times compared to the state-of-art methods without sacrificing performance.