An Efficient Method for Video Shot Transition Detection Using Probability Binary Weight Approach

Nandini, H. M.; Chethan, H. K.; Rashmi, B. S.

doi:10.4018/ijcvip.2021070101

Cited by 4 publications

(3 citation statements)

References 32 publications

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“…Abrupt lens refers to the direct splicing of two lenses at the junction without any transition. Gradual lens refers to the fusion of the last frames of the first lens and the front frames of the last lens at the junction through the processing of computer special effects, resulting in a slow transition [3].…”

Section: Film and Television Postproduction Methodsmentioning

confidence: 99%

Research on Postproduction of Film and Television Based on Computer Multimedia Technology

Yan

Wang

2022

Scientific Programming

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In the postproduction of film and television, the fast movement of objects or camera shake will lead to an excessive selection of key frames, resulting in information redundancy and affecting the stability of postproduction videos and video-editing effects. Aiming at this problem, this paper proposes a method of film and television postproduction based on digital media technology. Firstly, the film and video shots are segmented to find out the changing scenes; secondly, the bottom feature vector of each image is formed according to the color space entropy, motion vector, and motion region, and the video key frame is extracted based on digital media technology; finally, the pixel label information is established in the key frame, and the feature points located in the foreground are removed, while the time feature matching points and spatial feature matching points in the background are left, so as to achieve dynamic video mosaic. The experimental results show that the film and television postproduction method based on digital media technology has strong advantages compared with other methods, which can improve the stability score of video and reduce the average stitching error.

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Section: Film and Television Postproduction Methodsmentioning

confidence: 99%

Research on Postproduction of Film and Television Based on Computer Multimedia Technology

Yan

Wang

2022

Scientific Programming

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“…However, histogram-based methods are limited to high-level motion activity due to their fast color informationchanging nature. The visual feature extraction-based methods performed well in detecting cut [42] and gradual transitions [43]. However, this approach caused the problem of missdetection in high-motion activity frames such as camera pan, zoom-in/out, and large object movements.…”

Section: B Scene Transition Pattern Feature-based Approachmentioning

confidence: 99%

Semantic Scene Object-Camera Motion Recognition for Scene Transition Detection Using Dense Spatial Frame Segments and Temporal Trajectory Analysis

Chakraborty,

Chiracharit,

Chamnongthai

2024

IEEE Access

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Semantic video scene-understanding applications rely on object-camera motion recognition techniques for scene contextual movement representation. While existing machine learning-based methods perform efficiently, their primary limitation is to analyze motion patterns from normal frames only, neglecting the scene transition frames. This causes significant false alarms due to the undetected objectcamera motion patterns during scene transitions. In this paper, we propose a novel method for object and camera motion recognition of two consecutive scenes from their transition frames. First, our method detects cut transitions using principal component analysis (PCA) to segment the video into shots. Additionally, it eliminates large text transitions that are often falsely detected as cut transitions using structural similarity index measurement (SSIM) properties. Second, it selects candidate segments to localize normal and wipe transition frames using slope angle characteristics obtained from linear regression. Third, it extracts dense semantic spatial features at multi-scale using the modified DeepLabv3+ network to segment selected candidate frames into foreground, background, and wipe pixels. Finally, an optical flow algorithm-based temporal trajectory tracking model is applied on each segmented pixel to recognize the object, camera pan, zoom-in, and zoom-out motion patterns. We further remove falsely detected non-transition motion frames to improve wipe transition detection. The experimental results are obtained using the benchmark TRECVID and the multimedia datasets. The proposed method using pixel-level classification and temporal trajectory analysis achieved an average accuracy improvement of 9.28% for object-camera motion recognition, 3.75% for cut transition detection, and 3.01% for wipe transition detection.

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“…However, borders might move at any arbitrary speed and in any direction, such as horizontal, vertical, or diagonal directions, or according to geometrical shapes such as rectangular, circular, diamond or any random object's shape. Rule-based methods related to visual features can be performed by edge-based [15][18], histogram-based [12][17] [58], pixelbased [26][27], and visual rhythm based [20] algorithms. • Motion wipe transitions (WIM): In contrast, motion wipe transitions can be divided into two types.…”

Section: Introductionmentioning

confidence: 99%

Wipe Scene Change Detection in Object-Camera Motion Based on Linear Regression and an Inflated Spatial-Motion Neural Network

et al. 2023

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To facilitate content-based video analysis, automatic scene change detection (SCD) with large-scale motion activity is an essential fundamental step for locating a transition from one video scene to another. With the exponential increase in digital media usage, SCD has become more challenging in processing large motion content with minimal information loss and maximum perseverance. Wipe SCD in object-camera motion is noticeable evidence of this issue. Wipe transitions, which are a type of gradual transition, have diverse motion pattern changes when influenced by object-camera motion (camera pan, large-object, and zoom-in/out), creating a velocity imbalance in the same frame. Furthermore, this motion imbalance leads to false detection. Due to the loss of motion information and longer processing time of existing frameworks, we propose a novel method of wipe scene change detection (WSCD) based on deep spatial-motion feature analysis. First, large input videos are segmented into shots using dimensionality reduction and adaptive threshold. Secondly, linear regression is used to compute slope angle changes in shots for candidate selection and wipe localization. Finally, only selected candidates are processed to extract features using a two-stream inflated 3D-convolutional neural network for RGB stream and optical flow velocity for motion stream network (I3DCNN) and then classified into wipe in-motion and no-motion clips. The experimental results are obtained by classifying wipe patterns using a detection reviewing and merging strategy on corresponding wipe frames. The average improvement in wipe scene change detection accuracy evaluated on the benchmark TRECVID dataset is 11.9%, demonstrating the efficacy of our proposed method.

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An Efficient Method for Video Shot Transition Detection Using Probability Binary Weight Approach

Cited by 4 publications

References 32 publications

Research on Postproduction of Film and Television Based on Computer Multimedia Technology

Research on Postproduction of Film and Television Based on Computer Multimedia Technology

Semantic Scene Object-Camera Motion Recognition for Scene Transition Detection Using Dense Spatial Frame Segments and Temporal Trajectory Analysis

Wipe Scene Change Detection in Object-Camera Motion Based on Linear Regression and an Inflated Spatial-Motion Neural Network

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