Eagle Eye CBVR Based on Unique Key Frame Extraction and Deep Belief Neural Network

Prathiba, T.; Kumari, R. Shantha Selva

doi:10.1007/s11277-020-07721-4

Cited by 6 publications

(5 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In 2021, Prathiba, T and Kumari, R., [17] presented an Eagle Eye CBVR (EE-CBVR) approach that used innovative techniques for the three most crucial dimensions of CBVR. The histogram of oriented gradients (HOG), LBP, and coherence with progressive transition have been taken into account.…”

Section: Literature Surveymentioning

confidence: 99%

“…The performance of the proposed Novel Fuzzy entropy-based Leaky ShuffleNet CBVR system is analyzed using the F-measure, Accuracy, recall, precision, response time, and specificity. The effectiveness is evaluated in comparison to the existing FALKON [14], EE-CBVR [17], and ECBVR-ACNN [20] approaches. The following formulas can be used to calculate the performance measures mentioned above.…”

Section: Performance Analysismentioning

confidence: 99%

“…The accuracy rate reveals that the proposed method is more efficient than the existing techniques. The comparative analysis is performed between the proposed method and existing FALKON [14], EE-CBVR [17], and ECBVR-ACNN [20] methods. Fig.…”

Section: Comparative Analysismentioning

confidence: 99%

“…Fig. 12 compares the proposed method with the existing FALKON [14], EE-CBVR [17], and ECBVR-ACNN [20] methods. Figure 12 shows that the proposed strategy performs more accurately when compared to current methods.…”

Section: Comparative Analysismentioning

confidence: 99%

See 3 more Smart Citations

Novel Fuzzy Entropy Based Leaky Shufflenet Content Based Video Retrival System

Kavitha

Simon²,

Sumathy³

2023

Preprint

View full text Add to dashboard Cite

Developing Content-Based Video Retrieval (CBVR) for large-scale videos is a major challenge due to the enormous growth of video content on the internet. One of the major downfalls associated with CBVR is high search response time and low accuracy. In this paper, a Novel Fuzzy entropy based Leaky ShuffleNet CBVR system has been proposed to reduce the search response time and for high accuracy. The proposed system has three major phases i) Data Processing, ii) Feature Extraction and iii) Feature selection and iv) Similarity search. Initially, the video is processed using Apache storm to convert video into keyframes. Consequently, facial landmarks, head pose, and eye gaze, edges are extracted using various feature extraction techniques. The most relevant features are selected in the feature selection phase by using the Fuzzy entropy measure. Finally, based on the selected features Leaky ShuffleNet retrieve the relevant videos based on the user query. Experiments were performed on two different datasets such as Hollywood2 and UCF50 in three different setups (Single Node, Vertical Scaling, Horizontal Scaling). Several metrics were analyzed to measure the effectiveness of the suggested strategy, including recall, specificity, accuracy, precision, and the F-measure. According to experimental results, the proposed system has a search reaction time of 0.75 seconds, which is lower than the existing methods. The proposed method improves the overall accuracy by 1.2%, 2.5%, and 3.2% better than the existing ECBVR-ACNN, FALKON, and EE-CBVR respectively.

show abstract

Section: Literature Surveymentioning

confidence: 99%

Section: Performance Analysismentioning

confidence: 99%

Section: Comparative Analysismentioning

confidence: 99%

Section: Comparative Analysismentioning

confidence: 99%

See 2 more Smart Citations

Novel Fuzzy Entropy Based Leaky Shufflenet Content Based Video Retrival System

Kavitha

Simon²,

Sumathy³

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In recent years, the extraction and analysis of video information has become an important research content in video processing, which is of great significance in video semantic extraction, video query, and other aspects. Character detection and background detection, which are similar to scene information detection, have been deeply studied and widely applied [1][2][3][4][5][6][7][8][9]. However, there are not many indepth researches on video situational information.…”

Section: Introductionmentioning

confidence: 99%

Video Scene Information Detection Based on Entity Recognition

Qian

Dai

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Video situational information detection is widely used in the fields of video query, character anomaly detection, surveillance analysis, and so on. However, most of the existing researches pay much attention to the subject or video backgrounds, but little attention to the recognition of situational information. What is more, because there is no strong relation between the pixel information and the scene information of video data, it is difficult for computers to obtain corresponding high-level scene information through the low-level pixel information of video data. Video scene information detection is mainly to detect and analyze the multiple features in the video and mark the scenes in the video. It is aimed at automatically extracting video scene information from all kinds of original video data and realizing the recognition of scene information through “comprehensive consideration of pixel information and spatiotemporal continuity.” In order to solve the problem of transforming pixel information into scene information, this paper proposes a video scene information detection method based on entity recognition. This model integrates the spatiotemporal relationship between the video subject and object on the basis of entity recognition, so as to realize the recognition of scene information by establishing mapping relation. The effectiveness and accuracy of the model are verified by simulation experiments with the TV series as experimental data. The accuracy of this model in the simulation experiment can reach more than 85%.

show abstract

Ownership of abandoned object detection by integrating carried object recognition and context sensing

Russel,

Selvaraj

2023

Vis Comput

View full text Add to dashboard Cite

Eagle Eye CBVR Based on Unique Key Frame Extraction and Deep Belief Neural Network

Cited by 6 publications

References 39 publications

Novel Fuzzy Entropy Based Leaky Shufflenet Content Based Video Retrival System

Novel Fuzzy Entropy Based Leaky Shufflenet Content Based Video Retrival System

Video Scene Information Detection Based on Entity Recognition

Ownership of abandoned object detection by integrating carried object recognition and context sensing

Contact Info

Product

Resources

About