Diabetic Retinopathy Detection Using Machine Learning
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Monocular Depth Estimation using a Multi-grid Attention-based Model
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Speedy Image Crowd Counting by Light Weight Convolutional Neural Network
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Construction of Efficient Smart Voting Machine with Liveness Detection Module
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An Economical Robotic Arm for Playing Chess Using Visual Servoing
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Triplet loss for Chromosome Classification
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Unstructured Noise Removal for Industrial Sensor Imaging Unit by Hybrid Adaptive Median Algorithm
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Real Time Sign Language Recognition and Speech Generation
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Analysis of Artificial Intelligence based Image Classification Techniques
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Design of ANN Based Machine Learning Method for Crop Prediction
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A REVIEW ON IOT BASED MEDICAL IMAGING TECHNOLOGY FOR HEALTHCARE APPLICATIONS
Volume-1 | Issue-1
COMPUTER VISION BASED TRAFFIC SIGN SENSING FOR SMART TRANSPORT
Volume-1 | Issue-1
Diabetic Retinopathy Detection Using Machine Learning
Volume-4 | Issue-1
Accurate Segmentation for Low Resolution Satellite images by Discriminative Generative Adversarial Network for Identifying Agriculture Fields
Volume-3 | Issue-4
Deep Learning based Handwriting Recognition with Adversarial Feature Deformation and Regularization
Volume-3 | Issue-4
State of Art Survey on Plant Leaf Disease Detection
Volume-4 | Issue-2
Optimal Compression of Remote Sensing Images Using Deep Learning during Transmission of Data
Volume-3 | Issue-4
OverFeat Network Algorithm for Fabric Defect Detection in Textile Industry
Volume-3 | Issue-4
VIRTUAL RESTORATION OF DAMAGED ARCHEOLOGICAL ARTIFACTS OBTAINED FROM EXPEDITIONS USING 3D VISUALIZATION
Volume-1 | Issue-2
Two-Stage Frame Extraction in Video Analysis for Accurate Prediction of Object Tracking by Improved Deep Learning
Volume-3 | Issue-4
Volume - 3 | Issue - 4 | december 2021
Published
22 December, 2021
Recently, the information extraction from graphics and video summarizing using keyframes have benefited from a recent look at the visual content-based method. Analysis of keyframes in a movie may be done by extracting visual elements from the video clips. In order to accurately anticipate the path of an item in real-time, the visible components are utilized. The frame variations with low-level properties such as color and structure are the basis of the rapid and reliable approach. This research work contains 3 phases: preprocessing, two-stage extraction, and video prediction module. Besides, this framework on object track estimation uses the probabilistic deterministic process to arrive at an estimate of the object. Keyframes for the whole video sequence are extracted using a proposed two-stage feature extraction approach by CNN feature extraction. An alternate sequence is first constructed by comparing the color characteristics of neighboring frames in the original series to those of the generated one. When an alternate arrangement is compared to the final keyframe sequence, it is found that there are substantial structural changes between consecutive frames. Three keyframe extraction techniques based on on-time behavior have been employed in this study. A keyframe extraction optimization phase termed as "Adam" optimizer, dependent on the number of final keyframes is then introduced. The proposed technique outperforms the prior methods in computational cost and resilience across a wide range of video formats, video resolutions, and other parameters. Finally, this research compares SSIM, MAE, and RMSE performance metrics with the traditional approach.
KeywordsDeep learning object tracking Video object prediction key frame prediction optimization
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