Effective security solutions for consumer applications require significant advances in the field of video surveillance. Video synopsis (VS) has been a crucial tool in streamlining consumer surveillance investigations, through rapid assessment of the video data and projecting multiple objects simultaneously. The framework of VS is significantly impacted by the operation of the optimization module. Techniques such as simulated annealing (SA) is utilized to achieve the minimization of the energy. However, the real-time implications lead to a prolonged convergence time. This work proposes an approach to integrating the arithmetic optimization algorithm (AOA) into the VS framework. Achieving a globally optimal solution along with a faster convergence rate. The effectiveness of the proposal is estimated through various experimental evaluations and analyses. Thus, intelligent and effective reviewing of any surveillance video would be possible through the implementation of the proposed approach.

Surveillance videos are a ubiquitous and powerful tool in modern security and surveillance systems. Storing and analyzing these surveillance videos pose a challenge to both cost and time. Surveillance videos have a lot of spatio-temporal redundancies. Owing to this, video synopsis aims to reduce the redundancy to produce a summary, through the preservation of all activities of objects in a short span. Video synopsis has multiple steps, of which the optimization module is the main focus. Reducing activity loss, minimizing collision occurrences, and ensuring temporal consistency are some of the objectives that the energy minimization component within the video synopsis framework serves. This paper measures and studies the performances of various standalone (generic) algorithms and hybrid algorithms. Comprehensive experiments are conducted, and the outcomes are analyzed to assess their effectiveness, considering the reduction of activity loss, and collision occurrences, and ensuring temporal consistency. This paper highlights the practical application of optimization algorithms and emphasizes the significance of choosing the right optimization algorithm to minimize energy when creating the synopsis of an object-based surveillance video.

Healthcare is experiencing a rapid increase in medical records, requiring confidentiality and data integrity. Privacy plays a significant role in healthcare security. In general, users used to share data through insecure channels, and attacks may occur. Therefore, the healthcare system should ensure a secure authentication process. The proposed model uses blockchain technology to share and store patient data securely. For secure data access, the authentication process is to be performed between users and the blockchain using cryptographic algorithms. The proposed protocol uses the zero-knowledge proof (ZKP) embedded with the post-quantum cryptography technique NTRUEncrypt for the authenticating process, which is resistant to all attacks. The security analysis of the proposed protocol is performed through formal security verification using the Scyther tool and informal security analysis. The security analysis proves that the proposed protocol is resistant to well-known attacks. In addition, the proposed protocol provides better performance than existing models.

A secret image transmission technique has been put forward in this paper using (3,3) visual cryptographic shares generated from secret image that are fabricated into frames in Graphics Interchange Format (GIFs) to prevent any intruder from knowing the secret contained in the GIFs. The (3, 3) Visual Cryptography technique creates shares from a binary secret image. Using GIFs as communication hosts, each share has been embedded into the Least Significant Bit (LSB) of the pixels of any single meaningful frame of GIFs. The shares obtained from the corresponding meaningful frames of the GIFs, during decoding, are combined to create the authenticated image. The combination of visual cryptography (VC) technique and steganographic principles ensures not only the secure distribution of shares but also adds an extra layer of protection through the integration of the GIF format.

Nowadays, surveillance cameras are ruling the world in terms of security measures. Worldwide application of surveillance cameras leads to the production of huge amounts of video data. Investigating and getting the necessary information from such a lengthy video is a challenging task. To ease the inspection, Video Synopsis (VS) technology was proposed. Video Synopsis is the technique used to shorten a long-duration video with the preservation of all activities that have occurred originally. The synopsis of a video can be produced through four modules, namely, Object Detection, Object Tracking, Optimization, and finally Stitching. The efficient implementation of the VS is mainly dependent on the Optimization module. Mostly, Simulated Annealing (SA) is applied to solve the optimization problem in Object-based Single-camera Offline Surveillance Video Synopsis. In this paper, an attempt is made to implement and study the performance of the Driving Training Based Optimization (DTBO) algorithm for solving the optimization problem. The experimental results of DTBO are compared with the performance of the SA, Non-dominated Sorting Genetic Algorithm II (NSGA-II), Forest Optimization Algorithm (FOA), Elltist-Jaya, and SAMP-Jaya Algorithm. From the results and comparison, it is observed that the effectiveness of the DTBO algorithm in minimizing the energy function of Object-based Single-camera Offline Surveillance Video Synopsis is superior to the existing algorithms.

The present work describes a multi-area (two and three) renewable-energy-source-integrated thermal-hydro-wind power generation structure along with fleets of plug-in electrical vehicles (PEVs) in each control area. The generation–load balance is the prime objective, so automatic generation control (AGC) is adopted in the system. In the paper, a cascaded combination of proportional derivative with filter PDN and fractional-order proportional integral (FOPI) is proposed and tuned using the hybrid chemical reaction optimization with pattern search (hCRO-PS) algorithm. The hCRO-PS algorithm is designed successfully, and its effectiveness is checked through its application to various benchmark functions. Further, Eigen value analysis is carried out for the test system to verify the system stability. The impacts of diverse step load perturbation (i.e., case I, II, III, and IV) and time-varying load perturbation are also included in the study. Moreover, the impact of renewable sources, PEVs in different areas, and varied state of charge (SOC) levels on the system dynamics are reflected in the work. From the analysis, it can be inferred that the proposed controller provides comparable results with other fractional-order and conventional controllers under varying loading conditions.

Video synopsis is a promising technology that offers easy browsing and indexing of surveillance videos. This article presents an improved video synopsis framework, introducing the inclusion of anomalous tube detection module. The suggested framework performs better than the existing methodologies by offering a flexibility to the user to generate a synopsis video, which is based on user's choice of interest. Traditionally, to generate a synopsis video, the object tubes are temporally shifted for achieving compression. The applied temporal shift incurs a large number of collision artifacts along with temporal chronology violation. To address this issue for producing a visually comfortable synopsis video, the collision and temporal chronology violations are amended through Acceleration/Retardation of object's motion and spatial shift. Collision oriented Acceleration/Retardation and Spatial shift strategies are embedded sequentially in the proposed combined algorithm cSAScO. The unified representation of the proposed cSAScO algorithm combines the individual strength of Simulated Annealing (SA) and Scenario Optimization (ScO) and is employed to the formulated choice-based tube rearrangement problem. The efficacy of the proposed scheme is demonstrated through extensive experiments and its performance compared with that of the benchmark schemes.

The concept of a smart grid is an integration of efficient storage system plug-in electric vehicle (PEV) to the existing grids. The application of any storage technique increases the dimension and complication of the conventional grid. Thus, to realize a smart grid scenario, the renewable energy and PEVs are incorporated and tested in this study. Here, assisting the AGC for maintaining the frequency of a Jaya optimized controller, additional filter integrated proportional-integral-derivative (nPID) is employed. A two-area interrelated network has been analyzed with hydel, thermal, gas power units and PEV in both areas. A load deviation of 10% has been considered in each area. The system is verified with various controllers such as PI, PID, and nPID, and it is noteworthy that the reaction period of the system has improved, thus enhancing the stability when introduced with PEVs as a storage medium.

Video Synopsis is a smart and efficient solution to summarize a long duration of surveillance video into short. Most of the video synopsis techniques are not suitable to address complex situations like changes in illumination, dynamic background, camera jitter, etc. These techniques firmly depend on the preprocessing results of foreground extraction and multiple objects tracking. Further, the optimization process is a vital phase for the decrement of collision rate among moving objects, where the widely used Simulated Annealing (SA) usually suffers from the issue of slow convergence rate with a high computational overhead. Taking these aforementioned facts into account for feature extraction, we formulate a foreground extraction scheme exploring the concept of multi-frame and multi-scale in Generative Adversarial Network (mFS-GANs). Further, an optimization algorithm is proposed through the hybridization of SA and Grey Wolf Optimizer (GWO), named as, HGWOSA to ensure global optimal result with a low computing overhead. The performance of the proposed scheme is evaluated through extensive simulations and compared with that of the benchmark schemes. The experiments are carried out using some standard surveillance video dataset (ChangeDetection.Net, MIT Surveillance Dataset, and UMN Dataset) and one self-generated surveillance video at IIIT Bhubaneswar. Overall analysis and experimental evaluations demonstrate that our proposed scheme outperforms the other competing schemes in terms of both the quantitative and qualitative measures. Finally, the proposed model can be substantially employed in the generation of off-line video synopsis, which is potentially applicable to video surveillance applications for smart cities.

The integration of renewable energy with the grid is essential given the environmental and economic constraints. In this work, the dynamic performance of Automatic Generation Control (AGC) was studied with a dynamic steam turbine and renewable sources. A 2-area multi-non-conventional source (Thermal-Hydro-Solar-wind-Gas-battery storage) system with communication time delay (τ) is implemented with the proposed cascaded PID (C-PID) controller scheme. The parameters of the C-PID controller were tuned by a hybrid Improved Teaching Learning Based Optimization and Differential Evolution (hITLBO-DE). Root locus, Bode plot, and eigenvalue analysis were used to demonstrate the stability of the system. Various controllers (i.e. I, PI, PID, and C-PID) were implemented with the designed system under varying operating conditions of step (1% to 10%) and dynamic load variation (1%, ±1%). The C-PID controller is able to maintain a stable system response even for τ of 4 s. Finally, the obtained Amelioration (%) of 55.93%, 49.03%, and 72.84% for Over Shoot emphases the efficacy of the proposed C-PID controller and hITLBO-DE algorithm.

Alongside autonomous submissions, foreground extraction is considered to be the foundation for various video content analysis technologies, like moving object tracking, video surveillance and video summarization. This paper proposes an efficient foreground extraction methodology based on conditional Generative Adversarial Network. The proposed generator, which is made up of two networks working in series- Foreground Extractor and Segmentation Network, maps the video frames to corresponding foreground masks. The discriminator aids the learning of generator by learning to differentiate between seemingly real and fake foreground maps. The method used a multi-scale approach in order to capture robust features across multiple scales of input using the Feature Extractor Network, which are then used by the successive Segmentation Network to produce the final foreground map. In addition, a multi-frame approach is also used to facilitate capturing of appropriate temporal features. The performance of the proposed model is evaluated on CDnet 2014 Dataset and outperforms existing methods.

In the age of the smart city, each activity is under surveillance. The employment of plentiful surveillance video cameras produces the gigantic amount of redundant video data. For ease of investigations, video synopsis competently shrinks the length with the preservation of all activities presents in the original video. The outcome of the video synopsis technology greatly depends on the central module, the optimization framework, and its minimization. This paper evaluates the performance of various optimization techniques, namely simulated annealing (SA), NSGA II, cultural algorithm (CA), teaching–learning-based optimization (TLBO), gray wolf optimizer (GWO), forest optimization algorithm (FOA), JAYA algorithm, elitist-JAYA algorithm, self-adaptive multi-population-based JAYA algorithm (SAMP-JAYA), to minimize the energy in the field of object-based surveillance video synopsis. The experimental results and analysis direct the need for an optimization algorithm which can efficiently and consistently solve the minimization problem in connection to video synopsis.

Video surveillance cameras capture huge amount of data 24 hours a day. However, most of these videos contain redundant data which make the process difficult for browsing and analysis. A significant amount of research findings have been made in summarization of recorded video, but such schemes do not have much impact on video surveillance applications. On the contrary, video synopsis is a smart technology that preserves all the activities of every single object and projects them concurrently in a condensed time. The energy minimization module in video synopsis framework plays a vital role, which in turn minimizes the activity loss, number of collision and temporal consistency cost. In most of the reported schemes, Simulated Annealing (SA) algorithm is employed to solve the energy minimization problem. However, it suffers from slow convergence rate resulting in a high computational load to the system. In order to mitigate this issue, this article presents an improved energy minimization scheme using hybridization of SA and Teaching Learning based Optimization (TLBO) algorithms. The suggested framework for static surveillance video synopsis generation consists of four computational modules, namely, Object detection and segmentation, Tube formation, Optimization, and finally Stitching and the central focus is on the optimization module. Thus, the present work deals with an improved hybrid energy minimization problem to achieve global optimal solution with reduced computational time. The motivation behind hybridization (HSATLBO) is that TLBO algorithm has the ability to search rigorously, ensuring to reach the optimum solution with less computation. On the contrary, SA reaches the global optimum solution, but it may get disarrayed and miss some critical search points. Exhaustive experiments are carried out and results compared with that of benchmark schemes in terms of minimizing the activity, collision and temporal consistency costs. All the experiments are conducted on five widely used videos taken from standard surveillance video data set (PETS 2001, MIT Surveillance Dataset, ChangeDetection.Net, PETS 2006 and UMN Dataset) as well as one real generated surveillance video from the IIIT Bhubaneswar Surveillance Dataset. To make a fair comparison, additionally, performance of the proposed hybrid scheme to solve video synopsis optimization problem is also compared with that of the other benchmark functions. Experimental evaluation and analysis confirm that the proposed scheme outperforms other state-of-the-art approaches. Finally, the suggested scheme can be easily and reliably deployed in the off-line video synopsis generation.

Video Surveillance is an active area of research and provides promising security measures for consumer applications. To ease consumer surveillance investigations, Video Synopsis (VS) serves as a powerful tool to assess hours of video in a shorter retro of time by projecting multiple objects concurrently. The optimization module in VS framework is considered to be a key module, yet to date, only traditional optimization techniques have been addressed for energy minimization. Amongst these, simulated annealing (SA) has been broadly employed to produce global optimal solution without getting trapped in local minima. However, the convergence time of SA is quite high as the next state is chosen randomly to achieve real-time performance. This article presents an improved energy minimization scheme using hybridization of SA and JAYA algorithm to achieve global optimal solution with faster convergence rate. The weights associated with the energy function are computed using analytic hierarchy process (AHP) instead of heuristic selection. From experimental evaluations and analysis, it is seen that the proposed scheme exhibits superior performance to minimize the overall energy cost with lesser computational time. The proposed scheme has a potential to quickly review consumer surveillance video data in a smart and efficient way.

Video surveillance is one of the most demanding technology to combat security threats. With the huge amount of video data, the traditional surveillance solutions fail to review volumes of video data quickly. Hence, to mitigate this problem, video synopsis is an ultimate choice to make the video surveillance smart, automated and intelligent. It serves as a powerful tool to assess hours of video in a shorter retro of time by projecting multiple objects concurrently. The present article provides a comprehensive, categorical review based on single-camera surveillance video synopsis methodologies with a focus to develop an insight into the current research trends. This review concentrates on the frameworks for off-line/on-line, event/cluster-based, quality enhancement point of view, query-based, and tube rearrangement/placement problem. A comparative analysis is also carried out in terms of the methodology used and potential application. In conclusion, different open research issues related to Video Synopsis Technology are compared and projected in several ways for future research.

In the proposed technique, the first idea of a novel elucidation has been proposed to shelter dull Passport. The contemporary state of dull Passport be defeated by proper Passport verification along with Passport owner authentication. The proposed solution space ensures security against these two which is candidly adoptable in the current milieu of India. As Verification and Authentication are two special stream of Cryptography; the only way to lock facts, proposed contrivance is based on Visual Cryptography and Palm Vein based authentication practice. As a fact of multimedia data hiding, Visual Cryptography is a well-known method of encrypting images into several shares where stacking corresponding shares reveals secrets without intervention of any electronic devices. Palm Vein based biometric is already implemented for user authentication in many countries throughout the world. It is an on-going technology which is very easy to implement and cost effective with respect to resources and time. The said proposal will verify dull Passport through Visual Cryptography and authenticate dull Passport holder at various checkpoints through Palm Vein Authentication in the form of a Multilayer cryptographic technique. The performance of the proposed Visual Cryptographic algorithm is also compared with some existing algorithms. The comparisons shows that the proposed algorithm is better than the existing algorithms with respect to space requirement, Meaningfulness, and security. © 2013 IEEE.

In this paper a secret message/image transmission technique has been proposed through (2, 2) visual cryptographic shares which are covered by meaningful images so that a potential eavesdropper won't know there's a message to be read. A binary image is taken as cover image and authenticating message/image has been fabricated into it through a hash function where two bits in each pixel within four bits from LSB of the pixel is embedded and as a result it converts the binary image to gray scale one. (2, 2) visual cryptographic shares are generated from this converted gray scale image and these shares are hidden into separate meaningful images. During decoding shares are fetched from received meaningful images and combined to regenerate the authenticated image from where the secret message/image is obtained through the same hash function along with reduction of noise. Noise reduction is also done on regenerated authenticated image to produce original cover image at destination. © 2011 IEEE.

In this paper an efficient (2, 2) secret sharing scheme has been proposed where the generated shares are meaningful and the aspect ratio and image dimension of the shares remain constant with respect to the source image. In the CARVCMS, instead of generating new pixels for shares, the shares are generated by random inversion of the levels of the pixel values. The scheme is more secured and very easy to implement like other existing techniques of visual cryptography. At the receiving end, the secret is revealed directly through human visual system by stacking the shares in arbitrary order with proper alignment. To expound the efficiency of the proposed technique, the results are compared with the technique developed by Naor and Shamir (1995), Hegde et al. (2008) and Jena and Jena (2009) where it has been shown that CARVCMS gives better performance. © 2011 IEEE.

In this paper a novel (2, m + 1) visual cryptographic technique has been proposed, where m number of secret images has been encrypted based on a randomly generated master as a common share for all secrets which is decodable with any of the shares in conjunction with master share out of m + 1 generated shares. Instead of generating new pixels for share except the master share, hamming weight of the blocks of the secret images has been modified using random function to generate shares corresponding to the secrets. The proposed scheme is secure and very easy to implement like other existing techniques of visual cryptography. At the decoding end the secrets are revealed by stacking the master share on any one share corresponding to the secrets in any arbitrary order with proper alignment directly by human visual system where shares are printed on different transparencies which conforms the optimality of using shares. The aspect ratio and dimension of the secret images and the generated shares with respect to the source images remains constant during the process. © 2011 IEEE.