scholarly journals Motion Key frame Extraction Based on Grey Wolf Optimization Algorithm

2018 ◽  
Vol 232 ◽  
pp. 03032
Author(s):  
Yi Zhang ◽  
Juan Li ◽  
Min Zhang
Keyword(s):  
Fitness Function ◽  
Reconstruction Error ◽  
Human Motion ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Key Frame Extraction ◽  
Key Frame ◽  
Motion Data ◽  
Key Frames ◽  
Original Motion

In order to extract the key frames more effectively, we propose a key frame extraction method for human motion sequences based on Grey Wolf Optimization (GWO) algorithm. The fitness function is defined with the minimum reconstruction error and the optimal compression rate. The social hierarchy of grey wolves and hunting strategy are simulated to search key frames. Experimental results show that the proposed method can not only maintain the consistency of key frames between similar human motion sequences, but also effectively compress and summarize the original motion data. Under the same compression ratio, the reconstruction error is the minimum.

scholarly journals Within and Between Shot Information Utilisation in Video Key Frame Extraction

2011 ◽  
Vol 10 (03) ◽  
pp. 247-259 ◽  
Author(s):  
Dianting Liu ◽  
Mei-Ling Shyu ◽  
Chao Chen ◽  
Shu-Ching Chen
Keyword(s):  
Extraction Method ◽  
Video Sequence ◽  
Video Retrieval ◽  
Video Browsing ◽  
High Quality ◽  
Key Frame Extraction ◽  
Key Frame ◽  
Video Shot ◽  
Key Frames ◽  
Candidate Set

In consequence of the popularity of family video recorders and the surge of Web 2.0, increasing amounts of videos have made the management and integration of the information in videos an urgent and important issue in video retrieval. Key frames, as a high-quality summary of videos, play an important role in the areas of video browsing, searching, categorisation, and indexing. An effective set of key frames should include major objects and events of the video sequence, and should contain minimum content redundancies. In this paper, an innovative key frame extraction method is proposed to select representative key frames for a video. By analysing the differences between frames and utilising the clustering technique, a set of key frame candidates (KFCs) is first selected at the shot level, and then the information within a video shot and between video shots is used to filter the candidate set to generate the final set of key frames. Experimental results on the TRECVID 2007 video dataset have demonstrated the effectiveness of our proposed key frame extraction method in terms of the percentage of the extracted key frames and the retrieval precision.

A Novel Framework for Efficient Extraction of Meaningful Key Frames From Surveillance Video

2019 ◽  
pp. 342-359
Author(s):  
Suresh Chandra Raikwar ◽  
Charul Bhatnagar ◽  
Anand Singh Jalal
Keyword(s):  
Computational Cost ◽  
Extraction Methods ◽  
Surveillance Video ◽  
Distribution Method ◽  
Color Distribution ◽  
Key Frame Extraction ◽  
Key Frame ◽  
Efficient Extraction ◽  
Key Frames ◽  
Edge Histogram

The key frame extraction, aimed at reducing the amount of information from a surveillance video for analysis by human. The key frame is an important frame of a video to provide an overview of the video. Extraction of key frames from surveillance video is of great interest in effective monitoring and later analysis of video. The computational cost of the existing methods of key frame extraction is very high. The proposed method is a framework for Key frame extraction from a long surveillance video with significantly reduced computational cost. The proposed framework incorporates human intelligence in the process of key frame extraction. The results of proposed framework are compared with the results of IMARS (IBM multimedia analysis and retrieval system), results of the key frame extraction methods based on entropy difference method, spatial color distribution method and edge histogram descriptor method. The proposed framework has been objectively evaluated by fidelity. The experimental results demonstrate evidence of the effectiveness of the proposed approach.

scholarly journals A Deep Belief Network Combined with Modified Grey Wolf Optimization Algorithm for PM2.5 Concentration Prediction

2019 ◽  
Vol 9 (18) ◽  
pp. 3765 ◽  
Author(s):  
Yin Xing ◽  
Jianping Yue ◽  
Chuang Chen ◽  
Yunfei Xiang ◽  
Yang Chen ◽  
...  
Keyword(s):  
Prediction Accuracy ◽  
Prediction Models ◽  
Fitness Function ◽  
Deep Belief Network ◽  
Structure Parameters ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Belief Network ◽  
Concentration Prediction ◽  
Pm2.5 Concentration

Accurate PM2.5 concentration prediction is crucial for protecting public health and improving air quality. As a popular deep learning model, deep belief network (DBN) for PM2.5 concentration prediction has received increasing attention due to its effectiveness. However, the DBN structure parameters that have a significant impact on prediction accuracy and computation time are hard to be determined. To address this issue, a modified grey wolf optimization (MGWO) algorithm is proposed to optimize the DBN structure parameters containing number of hidden nodes, learning rate, and momentum coefficient. The methodology modifies the basic grey wolf optimization (GWO) algorithm using the nonlinear convergence and position update strategies, and then utilizes the training error of the DBN to calculate the fitness function of the MGWO algorithm. Through the multiple iterations, the optimal structure parameters are obtained, and a suitable predictor is finally generated. The proposed prediction model is validated on a real application case. Compared with the other prediction models, experimental results show that the proposed model has a simpler structure but higher prediction accuracy.

scholarly journals A Meaningful Compact Key Frames Extraction in Complex Video Shots

2017 ◽  
Vol 7 (3) ◽  
pp. 818
Author(s):  
Manar Abduljabbar Mizher ◽  
Mei Choo Ang ◽  
Ahmad Abdel Jabbar Mazhar
Keyword(s):  
State Of The Art ◽  
The State ◽  
Worst Case ◽  
Key Frame Extraction ◽  
Key Frame ◽  
Easy Problem ◽  
Extraction Algorithm ◽  
Key Frames ◽  
High Level ◽  
The Relationship

Key frame extraction is an essential technique in the computer vision field. The extracted key frames should brief the salient events with an excellent feasibility, great efficiency, and with a high-level of robustness. Thus, it is not an easy problem to solve because it is attributed to many visual features. This paper intends to solve this problem by investigating the relationship between these features detection and the accuracy of key frames extraction techniques using TRIZ. An improved algorithm for key frame extraction was then proposed based on an accumulative optical flow with a self-adaptive threshold (AOF_ST) as recommended in TRIZ inventive principles. Several video shots including original and forgery videos with complex conditions are used to verify the experimental results. The comparison of our results with the-state-of-the-art algorithms results showed that the proposed extraction algorithm can accurately brief the videos and generated a meaningful compact count number of key frames. On top of that, our proposed algorithm achieves 124.4 and 31.4 for best and worst case in KTH dataset extracted key frames in terms of compression rate, while the the-state-of-the-art algorithms achieved 8.90 in the best case.

scholarly journals A Novel Key-Frame Extraction Approach for Both Video Summary and Video Index

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Shaoshuai Lei ◽  
Gang Xie ◽  
Gaowei Yan
Keyword(s):  
Human Perception ◽  
Extraction Methods ◽  
Experimental Results ◽  
Semantic Structure ◽  
New Approach ◽  
Key Frame Extraction ◽  
Key Frame ◽  
Video Summary ◽  
Key Frames ◽  
Svd Decomposition

Existing key-frame extraction methods are basically video summary oriented; yet the index task of key-frames is ignored. This paper presents a novel key-frame extraction approach which can be available for both video summary and video index. First a dynamic distance separability algorithm is advanced to divide a shot into subshots based on semantic structure, and then appropriate key-frames are extracted in each subshot by SVD decomposition. Finally, three evaluation indicators are proposed to evaluate the performance of the new approach. Experimental results show that the proposed approach achieves good semantic structure for semantics-based video index and meanwhile produces video summary consistent with human perception.

A Novel Retrieval Method Based on Semantic Motion

2012 ◽  
Vol 468-471 ◽  
pp. 103-106 ◽  
Author(s):  
Jian Xiang
Keyword(s):  
Motion Capture ◽  
Three Dimensional ◽  
Human Motion ◽  
Retrieval Algorithm ◽  
Widespread Application ◽  
Retrieval Method ◽  
Key Frame ◽  
Motion Data ◽  
Motion Retrieval ◽  
3D Retrieval

With the widespread application of motion capture technology, three-dimensional(3D) motion data can be directly obtained. In this paper, a new 3D retrieval algorithm based on semantic motion is proposed. With semantic motion template and extracting key-frame, 3D human motion database can be processed effectively by retrieval. Experiment results show that our method can improve the accuracy of motion retrieval.

A Novel Framework for Efficient Extraction of Meaningful Key Frames from Surveillance Video

2015 ◽  
Vol 4 (2) ◽  
pp. 56-73 ◽  
Author(s):  
Suresh Chandra Raikwar ◽  
Charul Bhatnagar ◽  
Anand Singh Jalal
Keyword(s):  
Computational Cost ◽  
Extraction Methods ◽  
Surveillance Video ◽  
Distribution Method ◽  
Color Distribution ◽  
Key Frame Extraction ◽  
Key Frame ◽  
Efficient Extraction ◽  
Key Frames ◽  
Edge Histogram

The key frame extraction, aimed at reducing the amount of information from a surveillance video for analysis by human. The key frame is an important frame of a video to provide an overview of the video. Extraction of key frames from surveillance video is of great interest in effective monitoring and later analysis of video. The computational cost of the existing methods of key frame extraction is very high. The proposed method is a framework for Key frame extraction from a long surveillance video with significantly reduced computational cost. The proposed framework incorporates human intelligence in the process of key frame extraction. The results of proposed framework are compared with the results of IMARS (IBM multimedia analysis and retrieval system), results of the key frame extraction methods based on entropy difference method, spatial color distribution method and edge histogram descriptor method. The proposed framework has been objectively evaluated by fidelity. The experimental results demonstrate evidence of the effectiveness of the proposed approach.

scholarly journals A Fast and Robust Key Frame Extraction Method for Video Copyright Protection

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Yunyu Shi ◽  
Haisheng Yang ◽  
Ming Gong ◽  
Xiang Liu ◽  
Yongxiang Xia
Keyword(s):  
Copyright Protection ◽  
Extraction Method ◽  
Video Sequence ◽  
Characteristic Difference ◽  
Original Sequence ◽  
Key Frame Extraction ◽  
Key Frame ◽  
Frame Sequence ◽  
Frame Difference ◽  
Key Frames

The paper proposes a key frame extraction method for video copyright protection. The fast and robust method is based on frame difference with low level features, including color feature and structure feature. A two-stage method is used to extract accurate key frames to cover the content for the whole video sequence. Firstly, an alternative sequence is got based on color characteristic difference between adjacent frames from original sequence. Secondly, by analyzing structural characteristic difference between adjacent frames from the alternative sequence, the final key frame sequence is obtained. And then, an optimization step is added based on the number of final key frames in order to ensure the effectiveness of key frame extraction. Compared with the previous methods, the proposed method has advantage in computation complexity and robustness on several video formats, video resolution, and so on.

A New Algorithm of Video Key Frame Extraction Based on the Block of Frame Image

2012 ◽  
Vol 532-533 ◽  
pp. 1670-1674
Author(s):  
Hong Cai Feng ◽  
Yong Gong ◽  
Wei Gang Hu
Keyword(s):  
Correlation Coefficient ◽  
Extraction Efficiency ◽  
Image Block ◽  
Key Frame Extraction ◽  
Image Frame ◽  
Key Frame ◽  
Local Characteristics ◽  
Extraction Algorithm ◽  
Key Frames ◽  
The Stability

In order to improve the stability and effectiveness of the key frame extraction, we devise a key frame extraction algorithm based on frame image block. It get the local characteristics of the image frame information by partitioning frame image, then calculate the non-correlation coefficient, and extract key frames reflecting more diversified information. Experimental results show that the algorithm for the lens of key frames of extraction efficiency is higher and extraction of key frames can effectively reflect lens content.

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