Two-Stage Frame Extraction in Video Analysis for Accurate Prediction of Object Tracking by Improved Deep Learning

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.

A novel keyframe extraction method for video classification using deep neural networks

Combining convolutional neural networks (CNNs) and recurrent neural networks (RNNs) produces a powerful architecture for video classification problems as spatial–temporal information can be processed simultaneously and effectively. Using transfer learning, this paper presents a comparative study to investigate how temporal information can be utilized to improve the performance of video classification when CNNs and RNNs are combined in various architectures. To enhance the performance of the identified architecture for effective combination of CNN and RNN, a novel action template-based keyframe extraction method is proposed by identifying the informative region of each frame and selecting keyframes based on the similarity between those regions. Extensive experiments on KTH and UCF-101 datasets with ConvLSTM-based video classifiers have been conducted. Experimental results are evaluated using one-way analysis of variance, which reveals the effectiveness of the proposed keyframe extraction method in the sense that it can significantly improve video classification accuracy.

Efficient secure surveillance on smart healthcare IoT system through cosine-transform encryption

The emerging technologies with IoT (Internet of Things) systems are elevated as a prototype and combination of the smart connectivity ecosystem. These ecosystems are appropriately connected in a smart healthcare system which are generating finest monitoring activities among the patients, well-organized diagnosis process, intensive support and care against the traditional healthcare operations. But facilitating these highly technological adaptations, the preserving personal information of the patients are on the risk with data leakage and privacy theft in the current revolution. Concerning secure protection and privacy theft of the patient’s information. We emphasized this paper on secure monitoring with the help of intelligently recorded summary’s keyframe extraction and applied two rounds lightweight cosine-transform encryption. This article includes firstly, a regimented process of keyframe extraction which is employed to retrieve meaningful frames of image through visual sensor with sending alert (quick notice) to authority. Secondly, employed two rounds of lightweight cosine-transform encryption operation of agreed (detected) keyframes to endure security and safety for the further any kinds of attacks from the adversary. The combined methodology corroborates highly usefulness with engendering appropriate results, little execution of encryption time (0.2277-0.2607), information entropy (7.9996), correlation coefficient (0.0010), robustness (NPCR 99.6383, UACI 33.3516), uniform histogram deviation (R 0.0359, G 0.0492, B 0.0582) and other well adopted secure ideology than any other keyframe or image encryption approaches. Furthermore, this incorporating method can effectively reduce vital communication cost, bandwidth issues, storage, data transmission cost and effective timely judicious analysis over the occurred activities and keep protection by using effective encryption methodology to remain attack free from any attacker or adversary, and provide confidentiality about patient’s privacy in the smart healthcare system.