An Improved Algorithm for Video Summarization – A Rank Based Approach

Video Summary Based on F-Sift, Tamura Textural and Middle Level Semantic Feature

Key frame extraction is an important manner of video summarization. It can be used to interpret video content quickly. Existing approaches first partition the entire video into video clips by shot boundary detection, and then, extract key frames by frame clustering. However, in most team-sport videos, a video clip usually includes many events, and it is difficult to extract the key frames related to all of these events accurately, because different events of a game shot can have features of similar appearance. As is well known, most events in team-sport videos are attack and defense conversions, which are related to global translation. Therefore, by using fine-grained partition based on the global motion, a shot could be further partitioned into more video clips, from which more key frames could be extracted and they are related to the events. In this study, global horizontal motion is introduced to further partition video clips into fine-grained video clips. Furthermore, global motion statistics are utilized to extract candidate key frames. Finally, the representative key frames are extracted based on the spatial–temporal consistence and hierarchical clustering, and the redundant frames are removed. A dataset called SportKF is built, which includes 25 videos of 197,878 frames in 112 min and 764 key frames from four types of sports (basketball, football, American football and field hockey). The experimental results demonstrate that the proposed scheme achieves state-of-the-art performance by introducing global motion statistics.

Key frame extraction based on global motion statistics for team-sport videos

Keyframes extraction, a fundamental problem in video processing and analysis, has remained a challenge to date. In this paper, we introduce a novel method to effectively extract keyframes of a video. It consists of four steps. At first, we generate initial clips for the classified frames, based on consistent content within a clip. Using empirical evidence, we design an adaptive window length for the frame difference processing which outputs the initial keyframes then. We further remove the frames with meaningless information (e.g., black screen) in initial clips and initial keyframes. To achieve satisfactory keyframes, we finally map the current keyframes to the space of current clips and optimize the keyframes based on similarity. Extensive experiments show that our method outperforms to state-of-the-art keyframe extraction techniques with an average of \(96.84\%\) on precision and \(81.55\%\) on \(F_1\).

A Cascaded Approach for Keyframes Extraction from Videos

An adaptive threshold shot detection algorithm based on improved block color features is proposed in this paper. This paper adopts an improved block color feature extraction method based on equal area of rectangular ring. Sub-block accumulative color histogram is extracted as color features and different weight for different rectangle rings is set in order to highlight the central part of frame. Then, adaptive threshold of detecting abrupt shot and gradual shot is calculated, and different detection modules is used according to the distance of the characteristics between frames. In the abrupt shots detection, several frames' frame difference and the edge shape features between adjacent frames are calculated to detect the flash. In the gradual shots detection, the discontinuous frame differences between the current frame and back frames are used to detect the boundary of the gradual shot. The experimental results show that this method has better effect to different types of video.

An adaptive threshold shot detection algorithm based on improved block color features

An improved key frame extraction algorithm based on the low-level features of image is proposed in this paper. Firstly, each frame is divided into equal area of rectangular ring. Secondly, sub-block accumulative color histogram is extracted as color features and different weight is set for different rectangle rings in order to highlight the central part of frame. Thirdly, key frames are selected according to the significant change of frames. Lastly, the algorithm is optimized and key frames are selected in accordance with the frames of location in the video. The experimental results show that the proposed algorithm has good adaptability and can effectively reduce the redundant key frames when the shot has a sudden flash or the object moves fast.

Key frame extraction based on improved frame blocks features and second extraction

To improve the robustness and accuracy of the corner-detection algorithm, this paper proposes a camera-calibration method based on the EDLines algorithm for the automatic detection of chessboard corners. The EDLines algorithm is initially used to perform straight-line detection on the calibration image. The features of the broken straight lines at the corners are then used to filter the straight lines and remove the background straight lines outside the chessboard. The pixels in the rectangular area around the filtered straight line are sorted by the gray gradient. After using the sorted results to fit the straight line, the coordinates of the intersection of the straight lines are taken as the initial coordinates of the corners and perform subpixel optimization on them. Finally, the corner points are sorted by the conversion between pixel-coordinate systems. The camera exposure time changes and complex imaging-background experiments show that the algorithm has no missed detection and redundancy in corner detection. The average reprojection error is found to be less than 0.05 pixels, which can be used in actual calibration.

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Tao Li

Papers

Key frame extraction based on improved frame blocks features and second extraction

Chessboard Corner Detection Based on EDLines Algorithm