Shot Boundary Detection Problem Research Articles

Video shot-boundary detection: issues, challenges and solutions

The integration of high data transmission rates and the recent digital multimedia technology, paves the way to access a huge amount of video over the internet, in seconds. Additionally, uploading videos to different websites is no more confined to expert software professionals resulting in duplication of video data which led to exorbitant growth of multimedia information in cyberspace in a short span of time. This necessitates the development of efficient data management techniques including storage, searching and annotation mechanism. Automatic shot boundary detection is considered to be the first and foremost step towards such management. It is a booming area of research gaining attention in the domain of image processing, computer vision and pattern recognition. In this review paper, we present a detailed description of the methods and algorithms of shot boundary detection, reported in the last two decades. This review shows that using multiple features performs well in comparison to using only a single feature in the shot boundary detection problem although it leads to higher complexity. The major sources of disturbance in the boundary detection are the sudden illumination variation and presence of high motion in the video. An adaptive threshold outperforms a single global threshold in the boundary detection problem and the threshold requirement can be avoided through learning based strategies at the cost of larger training data and higher computation time. Moreover the present review includes a critical analysis of relative merits and demerits of existing algorithms and finally opens promising research directions in the area.

A motion and illumination resilient framework for automatic shot boundary detection

Detecting and locating a desired information in hefty amount of video data through manual procedure is very cumbersome. This necessitates segregation of large video into shots and finding the boundary between the shots. But shot boundary detection problem is unable to achieve satisfactory performance for video sequences consisting of flash light and complex object/camera motion. The proposed method is intended for recognising abrupt boundary between shots in the presence of motion and illumination change in an automatic way. Typically any scene change detection algorithm assimilates time separation in a shot resemblance metric. In this communication, absolute sum gradient orientation feature difference is matched to automatically generated threshold for sensing a cut. Experimental study on TRECVid 2001 data set and other publicly available data set certifies the potentiality of the proposed scheme that identifies scene boundaries efficiently, in a complex environment while preserving a good trade-off between recall and precision measure.

A Divide-And-Rule Scheme For Shot Boundary Detection Based on SIFT

Shot boundary detection is an important fundamental process toward automatic video indexing, retrieval, editing, etc. After a critical review of most approaches seeking to solve this problem, we propose a novel shot boundary detection. To improve the performance of the algorithm and reduce the computational cost, frames that are clearly not shot boundaries are first removed from the original video. After that, a novel SIFT keypoint matching algorithm based on SVM is proposed, which is used to capture the changing statistics of different kinds of shot transitions so as to identify, not only abrupt transitions, but also gradual transitions(fade, dissolve, wipe) accordingly. At last, our system use different algorithms for different kinds of shot transitions to help us to get a better solution for shot boundary detection problem. Numerical experiments in the evaluation of TRECVID and a variety of film videos demonstrate that our method is capable of accurately detecting shot transitions, and could greatly reduce the computational cost.

A Formal Study of Shot Boundary Detection

This paper conducts a formal study of the shot boundary detection problem. First, a general formal framework of shot boundary detection techniques is proposed. Three critical techniques, i.e., the representation of visual content, the construction of continuity signal and the classification of continuity values, are identified and formulated in the perspective of pattern recognition. Meanwhile, the major challenges to the framework are identified. Second, a comprehensive review of the existing approaches is conducted. The representative approaches are categorized and compared according to their roles in the formal framework. Based on the comparison of the existing approaches, optimal criteria for each module of the framework are discussed, which will provide practical guide for developing novel methods. Third, with all the above issues considered, we present a unified shot boundary detection system based on graph partition model. Extensive experiments are carried out on the platform of TRECVID. The experiments not only verify the optimal criteria discussed above, but also show that the proposed approach is among the best in the evaluation of TRECVID 2005. Finally, we conclude the paper and present some further discussions on what shot boundary detection can learn from other related fields

An effective post-refinement method for shot boundary detection

In content-based video analysis, shot boundary detection (SBD) is a common first step which segments video data into elementary shots, each comprising a sequence of consecutive frames recording a video event or scene continuous in time and space. Many SBD methods have been proposed in the literature, and experimental results show that the existing methods work reasonably well for abrupt shot boundaries, but less effectively for gradual shot boundaries. In this paper, we propose an effective post-refinement method for identifying actual shot boundaries from the results obtained by existing SBD methods. The proposed method formulates the SBD problem as sequential detection of changes in the underlying feature distributions whose parameters are estimated from existing video shots. Specifically, the proposed post-refinement method enhances the performance of SBD by identifying as many false positives (false detections) and false negatives (miss detections) as possible. Experiments conducted on a large set of test videos, whose initial shot boundaries are obtained by four existing SBD methods, show that the proposed post-refinement method can improve markedly the detection recall and precision and is rather insensitive to the thresholds used by the existing methods in detecting the initial shot boundaries.

Shot-boundary detection: unraveled and resolved?

Partitioning a video sequence into shots is the first step toward video-content analysis and content-based video browsing and retrieval. A video shot is defined as a series of interrelated consecutive frames taken contiguously by a single camera and representing a continuous action in time and space. As such, shots are considered to be the primitives for higher level content analysis, indexing, and classification. The objective of this paper is twofold. First, we analyze the shot-boundary detection problem in detail and identify major issues that need to be considered in order to solve this problem successfully. Then, we present a conceptual solution to the shot-boundary detection problem in which all issues identified in the previous step are considered. This solution is provided in the form of a statistical detector that is based on minimization of the average detection-error probability. We model the required statistical functions using a robust metric for visual content discontinuities (based on motion compensation) and take into account all (a priori) knowledge that we found relevant to shot-boundary detection. This knowledge includes the shot-length distribution, visual discontinuity patterns at shot boundaries, and characteristic temporal changes of visual features around a boundary. Major advantages of the proposed detector are its robust and sequence-independent performance, while there is also the possibility to detect different types of shot boundaries simultaneously. We demonstrate the performance of our detector regarding two most widely used types of shot boundaries: hard cuts and dissolves.