Scene Change Detection Algorithm Research Articles

Advanced Evaluation Method for Video Retrieval System

n the past few years, the multimedia storage has increased and costs of storing multi-media data has become cheaper. Which is why, there are large numbersof videos that are available in the video repositories. With development of the multi-media data types and available bandwidths.The proposed method transferseach video of database into scenes using color histogrambased scene change detection algorithm and key framesare extracted. For key frames multiple features areobtained using straight forward rules. A new framework depending on CNNs (convolutional neural networks) is recommended to perform the content based video retrieval with the less storage cost also with higher search capability. The recommended framework subsists of extraction algorithm with respect to key-frame and the feature collection strategies. Particularly, the extraction algorithm of key-frame takes benefit of clustering idea; so in that case excessive information is taken out from video data and also the storage cost is highly shortened. This work present a method uses the extracted features with convolutional neural network (CNN) for classification tasks. In this research paper, different types of videos will be used and the important features will be extracted using SIFT, after that we use the deep learning(CNN) process will be performed. The experimental results showed the efficiency and effectiveness of suggested approach

A novel video scene change detection using successive estimation of statistical measure and HiBiSLI method

Video data watermarking becomes very essential due to digital broadcasting. Scene change detection is the initial step, required for the digital video data copy protection. This paper presents novel scene change detection algorithm based on successive estimation of statistical measure [SESAME] and HiBiSLI algorithm. The designed system detects the scene change using, covariance difference between the measures of each frame is considered. Successive difference between the statistical measures of frames are used and the difference is compared with a pre-difference value. A new algorithm, a combination of histograms, binary search and linear interpolation [HiBiSLI] filtering algorithm is introduced to filter out similar frames at the initial stage. The filtering method uses calculated threshold value. Scene detection algorithm uses ffmpeg as reference to calculate different parameter values. The performance of the designed system is evaluated using precision, recall, F-measure values and computation time. The experimental result proves that the designed system is well good in terms of its computational time, low complexity and better parameter values.

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.

Keyframe Selection Framework Based on Visual and Excitement Features for Lifelog Image Sequences

Keyframe selection is the process of finding a set of representative frames from an image sequence. We aim to achieve an automatic keyframe selection. The main problem is that the accuracy of keyframe selection is highly subjective to each particular user. To deal with this problem, we propose a VIEMO keyframe selection framework based on visual and excitement features that consists of two integrated modules, namely; event segmentation and keyframe selection. Firstly, scene change detection algorithm was applied for an event segmentation. Later, visual features which are contrast, color variance, sharpness, noise and saliency along with excitement features from a biosensor are used to filter keyframe that closely matches with user selection keyframe. Two different fusion scheme which are flat and hierarchical fusion were also investigated. To evaluate the quality of keyframe from the proposed method, we present an evaluation techniques which grades the quality of the keyframe automatically. Even when the keyframe does not exactly match with the keyframe selected by the user, the degree of acceptance calculated from visual similarity is provided. Experimental results showed that keyframe selection using only visual features yielded an acceptance rate of \(74.16 \,\%\). Our proposed method achieves a higher acceptance rate of \(83.71\,\%\). Moreover, the acceptance rate was improved by the average of \(9.55\,\%\) in all participants. Therefore, our framework provides a potential solution to this subjective issue for keyframe selection in lifelog image sequences selection.

A Novel Robust Scene Change Detection Algorithm for Autonomous Robots Using Mixtures of Gaussians

Interest in change detection techniques has considerably increased during recent years in the field of autonomous robotics. This is partly because changes in a robot's working environment are useful for several robotic skills (e.g., spatial cognition, modelling or navigation) and applications (e.g., surveillance or guidance robots). Changes are usually detected by comparing current data provided by the robot's sensors with a previously known map or model of the environment. When the data consists of a large point cloud, dealing with it is a computationally expensive task, mainly due to the amount of points and the redundancy. Using Gaussian Mixture Models (GMM) instead of raw point clouds leads to a more compact feature space that can be used to efficiently process the input data. This allows us to successfully segment the set of 3D points acquired by the sensor and reduce the computational load of the change detection algorithm. However, the segmentation of the environment as a Mixture of Gaussians has some problems that need to be properly addressed. In this paper, a novel change detection algorithm is described in order to improve the robustness and computational cost of previous approaches. The proposal is based on the classic Expectation Maximization (EM) algorithm, for which different selection criteria are evaluated. As demonstrated in the experimental results section, the proposed change detection algorithm achieves the detection of changes in the robot's working environment faster and more accurately than similar approaches.

Balanced Parallel Scheduling for Video Encoding with Adaptive GOP Structure

Due to the nature of a dynamic group of picture (GOP) structure, parallel scheduling for video encoding becomes challenging. To address this, the balanced frame-level parallel scheduling algorithms are developed. The proposed approaches first determine the frame priority and then the thread priority assignment for scheduling. The concept of the algorithms lies in the analysis of coding complexity, temporal influence, and the required temporal burden to finish coding. To complete the scheduling with the dynamic GOP structure, a block-based abrupt and gradual scene change detection algorithm is also proposed to determine the GOP structure adaptively. The experiments show that the scheduling performance is close to the optimal. In addition, the concept of batch processing is incorporated so that the required buffer can be reduced.

Scene Change Detection Algorithm for H.264 using Dynamic Threshold and Fast Motion Estimation Algorithm based on PSO

Most of the existed scene change detection algorithms use fixed thresholds for finding the scene change. These thresholds are obtained by empirically or they must be calculated before the detection after the whole sequence is obtained. If Videos having high scene complexity and variation, then the Performance of most scene change algorithms decreases considerably. In this paper, we study the correlation between local statistical characteristics, scene duration and scene change. Based on this analysis, we further propose and implement a scene change algorithm for H.264 codec, defining an automated, dynamic threshold model with fast motion estimation algorithm with low complexity which can efficiently trace scene changes.

Efficient Scene Change Algorithm Based on Statistical Feature in the DCT Domain

In this paper, we propose an effective scene change detection algorithm directly in compressed domain. The proposed scene change algorithm(including abrupt change and ) test the n frame and n+1 frame through the extracting the feature of each frame. When extract the features of frame, two-dimensional statistical feature m1-s from DCT coefficients without its inverse transform was computed, Divide m1-s space into 42 unequal partitions (subspaces) and count the numbers pr within the 42 subspaces (entries) as the feature vector to judge the different of the two frames. locating scene changes is operated by comparison tests. In comparison with existing representative techniques, the experimental results show the superiority of the proposed method in terms of precision and processing speed.

An Optimized Dynamic Scene Change Detection Algorithm for H.264/AVC Encoded Video Sequences

Scene change detection plays an important role in a number of video applications, including video indexing, semantic features extraction, and, in general, pre- and post-processing operations. This paper deals with the design and performance evaluation of a dynamic scene change detector optimized for H.264/AVC encoded video sequences. The detector is based on a dynamic threshold that adaptively tracks different features of the video sequence, to increase the whole scheme accuracy in correctly locating true scene changes. The solution has been tested on suitable video sequences resembling real-world videos thanks to a number of different motion features, and has provided good performance without requiring an increase in decoder complexity. This is a valuable issue, considering the possible application of the proposed algorithm in post-processing operations, such as error concealment for video decoding in typical error prone video transmission environments, such as wireless networks.

Cost-effective scene change detection algorithm for real-time H.264 rate control

For frames involving an abrupt scene change, al- most all macroblocks are encoded using intramode in H.264/ AVC. Before encoding such frames, one must first determine whether a scene has changed to perform the appropriate rate control. Since the computation load of H.264/AVC is heavy, a more cost-effective algorithm for detecting scene changes is necessary for real-time operations. We propose such an algorithm for high-motion video, and the results show that the proposed algorithm reduces the computational load by 37 to 92% from existing algorithms. © 2008 Society of Photo-Optical Instrumentation Engineers.

Enhanced Eigen-Audioframes for Audiovisual Scene Change Detection

In this paper, a novel audio-visual scene change detection algorithm is presented and evaluated experimentally. An enhanced set of eigen-audioframes is created that is related to an audio signal subspace, where audio background changes are easily discovered. An analysis is presented that justifies why this subspace favors scene change detection. Additionally, a novel process is developed in order to detect audio scene change candidates in this subspace. Visual information is used to align audio scene change indications with neighboring video shot changes and, accordingly, to reduce the false alarm rate of the audio-only scene change detection. Moreover, video fade effects are identified and used independently in order to track scene changes. The false alarm rate is reduced further by extracting acoustic features in order to verify that the scene change indications are valid. The detection methodology was tested on newscast videos provided by the TRECVID2003 video test set. The experimental results demonstrate that the proposed method achieves an F-measure exceeding 0.85. Accordingly, it effectively tackles the scene change detection problem

Sudden scene change detection in compressed video using interpolated macroblocks in B-frames

This paper addresses an important area in video processing, namely compressed domain processing. For video indexing, video scene transition detection is an essential step to segment the video. Current techniques for scene change detection tend to suffer from a major limitation as most of them cannot identify scene transitions in the compressed domain. Since most video is expected to be stored in the compressed domain, scene transition detection in this domain is highly desirable. In this paper an algorithm for video scene change detection is proposed to overcome this limitation. In this scheme, properties of the B-frames are used as it is capable of measuring the correlation between two adjacent reference frames. The results show that this scheme performs better than schemes based on P-frames. Proposed scheme can be directly applied with compressed data with minimum decompression and hence it is computationally efficient and makes real time implementations possible. Results show that video scene transitions can be identified satisfactorily with the proposed scheme.

순차접근법을 이용한 MPEG 압축영역에서의 장면전환점 검출

최근까지 MPEG으로 압축된 영상의 장면전환점 검출에 관한 연구가 많이 진행되어왔다. 그러나 대체로 디코딩 과정을 거친 후 픽셀단위의 비교를 통해 장면전환점을 검출하는 경우가 많았다. 이 경우 디코딩 과정에서의 많은 계산에 의해 실시간 적용에 부적합하게 된다. 최근에는 압축영역에서의 정보만을 이용해 장면전환점을 검출하는 연구가 많이 진행되고 있으며, 본 논문에서는 압축영역에서 장면전환점을 검출하면서도 보다 빠르고 정확한 검출이 이루어지도록 I픽처 단위의 블록과 P픽처 단위의 내부블록으로 나눈 후 I블록별 검사를 통해 장면전환이 검출된 블록에 대해서 P블록별 검사를 수행하고 최종적으로 B적처까지의 검사를 통해 정확한 장면전환점을 찾는 순차접근법을 제안한다. 순차 접근법은 대부분의 처리가 압축영역에서 이루어지고 또한 블록단위의 결사를 하므로 불필요한 픽처들의 검사과정을 줄여서 보다 빠른 장면전환점 검출이 가능하다 순차접근법은 빠른 처리속도와 함께 정확한 검색이 가능하도록 최적의 알고리즘을 사용하고 있다. The study on scene change detection in the compressed MPEG videos has been done by various approaches. However, most of these approacher accomplished scene change detection by carrying out decoding processes and then by comparing pixels with pixels. This approach it not suitable for real time applications owing to much computing time of decoding processes. Recently, the study on scene change detection algorithms using only information of compressed domain is becoming Increasingly important. In this paper, we propose a sequential access method as an efficient scene change detection algorithm in the compressed domain. According to the type of pictures in the compressed MPEG video streams (divided in I-blocks and each I-block into P-blocks), the proposed algorithm provides effective scene change detection by applying sequential access and block by block mechanism. The proposed sequential access method provides fast and accurate detection operation by reducing checking procedures of unnecessary pictures due to molt of operations in compressed domain and checking by block units. Also, this approach uses optimal algorithm to provide fast and accurate detection operation.

Wipe scene change detection and classification in video sequences

There is an urgent need to extract key information auto- matically from video for the purposes of indexing, fast retrieval, and scene analysis. To support this vision, reliable scene change detec- tion algorithms must be developed. This paper describes a novel algorithm for wipe scene change detection in uncompressed and MPEG-1 compressed video sequences using statistical and geo- metric properties of each image. An efficient algorithm is also pro- posed to estimate statistical features in compressed video without full frame decompression. From uncompressed and MPEG com- pressed frames, thumbnails are obtained where pixels are the aver- ages and variances of luminance values of macroblocks. Differ- ences between thumbnails are computed and thresholded, and straight lines are detected in the resulting binary images. Persis- tence and motion of these lines indicate the presence of a shot transition using wipes. Results on video of various content types are reported and validated with the proposed schemes. Furthermore, results show that the accuracy of the detection is above 95% for uncompressed and above 90% for MPEG-1 compressed video.

Novel Error Concealment Method With Adaptive Prediction to the Abrupt and Gradual Scene Changes

In this paper, the impact of scene change on the conventional error concealment method is addressed and a novel error concealment method is proposed to improve the insufficiency of the conventional temporal error concealment algorithm due to the occurrence of scene change. Combining with the low complexity scene change detection algorithm using macroblock type information, the corrupt blocks resulting from bit errors are concealed either temporally or spatially depending on whether or not an abrupt scene change is found. In the case of gradual scene change, a novel error concealment method of interpolation and extrapolation is proposed to utilize the linear property of gradual scene change sequence, and effectively reduce the concealment error in comparison with the conventional algorithm. A great improvement of about 3 to 5 dB PSNR on average and 6 to 8 dB in some cases is obtained with very little memory and computation overhead.

Fast scene change detection for personal video recorder

We propose an efficient scene change detection method for personal video recorder to provide users with several convenient functionalities such as the browsing, fast forward and skip-play of recorded videos. Since most of the current available PVRs have low-powered CPUs, the scene change detector should not be computationally expensive. We implement the scene change detection algorithm using reduced images of only intra pictures in MPEG-2 compressed domain. The experimental results with a commercial PVR demonstrate the efficiency of the proposed method.

A hard disk drive embedded digital satellite receiver with scene change detector for video indexing

We present a hard disk drive embedded digital satellite receiver with a scene change detector for video indexing. We implement a scene change detection algorithm in the compressed domain for low computing power systems. This receiver can store, retrieve and manage the broadcast data by implementing an interface between the conventional digital satellite receiver and digital storage media. In addition, this receiver gives users an ability to search the scene change position by implementing a scene change detection algorithm. The detected temporal video segments are stored in the HDD and retrieved when users want them. The user can obtain more information for efficient video retrieval, using this proposed system.

Temporal Segmentation of MPEG Video Streams

Many algorithms for temporal video partitioning rely on the analysis of uncompressed video features. Since the information relevant to the partitioning process can be extracted directly from the MPEG compressed stream, higher efficiency can be achieved utilizing information from the MPEG compressed domain. This paper introduces a real-time algorithm for scene change detection that analyses the statistics of the macroblock features extracted directly from the MPEG stream. A method for extraction of the continuous frame difference that transforms the 3D video stream into a 1D curve is presented. This transform is then further employed to extract temporal units within the analysed video sequence. Results of computer simulations are reported.

동적 임계값과 컷 프레임 차를 이용한 점진적 전환 검출 기법

내용기반 검색을 위한 비디오 데이터 장면전환 검출에서 점진적인 전환을 검출하는 것은 갑작스런 전환을 검출하는 것에 비해 일반적으로 어려운 문제로 알려져 있다. 본 논문에서는 가변형 동적 임계값과 가장 최근에 검출된 컷 프레임과 현재 프레임간의 특징값 차이인 컷 프레임 차를 이용하여 갑작스런 전환과 점진적인 전환을 찾아내는 기법을 제안한다. 이를 위하여 본 논문에서는 점진적인 전환이 갖는 특성과 수학적 모델을 제시하고 컷 프레임 차를 이용하여 점진적인 전환을 검출할 수 있음을 보인다. 그리고 이를 바탕으로 갑작스런 전환과 점진적인 전환을 함께 검출할 수 있는 방법을 제시한다. 실세계 동영상 데이터를 대상으로 한 실험을 통해 제안하는 기법이 점진적인 전환 효과의 종류에 종속적이지 않으며 적은 연산 비용으로 쉽게 점진적인 전환 유무를 검출 할 수 있음을 보인다. Gradual scene change detection is known as more difficult problem then abrupt scene change detection on video data analysis for contents based retrieval. In this paper, we present a new method for scene change detection far both abrupt and gradual change using the variable dynamic threshold arid cut frame difference (CFD). For this, We present the characteristics arid mathematical models of gradual transitions anti then, how can be detected by the CFD. And also we present new scene change detection algorithm based on cut frame difference. By the experimental result using real world video data indicate that the proposed method detect various scene changes both abrupt and gradual change efficiently without time-consuming computation and any dependency on a kind of gradual change effects.

에지 영상의 분산을 이용한 비디오의 점진적 장면전환 검출

A new algorithm for gradual scene change detection in MPEG based frame sequences is proposed in this paper. The proposed algorithm is based on the fact that most of gradual curves can be characterized by variance distributions of edge information in the frame sequences. Average edge frame sequences are obtained by performing "sober" edge detection. Features are extracted by comparing variances with those of local blocks in the average edge frames. Those features are further processed by the opening operation to obtain smoothing variance curves. The lowest variance in the local frame sequences is chosen as a gradual detection point. Experimental results show that the proposed method provides 85% precision and 86% recall rate fur gradual scene changes.