scholarly journals Composite cellular automata based encryption method applied to surveillance videos

DYNA ◽  
2020 ◽  
Vol 87 (213) ◽  
pp. 212-221
Author(s):  
Luis Miguel Cortés Martinez ◽  
Luz Deicy Alvarado Nieto ◽  
Edilma Isabel Amaya Barrera
Keyword(s):  
Cellular Automata ◽  
Scientific Development ◽  
Video Frame ◽  
Surveillance Video ◽  
Surveillance Videos ◽  
Video Frames ◽  
And Performance ◽  
Reversible Cellular Automata ◽  
Encryption Method ◽  
Development Center

This work is part of the research project “Encryption Models Based on Chaotic Attractors” institutionalized in the Research and Scientific Development Center of the Universidad Distrital Francisco José de Caldas. In this paper, a symmetric encryption method for surveillance videos is presented, based on reversible composite cellular automata developed for this purpose. This method takes advantage of reversible cellular automata and elementary rule 30 properties, for efficient regions of interest encryption in surveillance video frames, obtaining an algorithm which experimental results of security and performance are consistent with those reported in current literature. In addition, it allows decryption without loss of information through a fixed size key for each video frame.

scholarly journals Post Event Investigation of Multi-stream Video Data Utilizing Hadoop Cluster

2018 ◽  
Vol 8 (6) ◽  
pp. 5089
Author(s):  
Jyoti Parsola ◽  
Durgaprasad Gangodkar ◽  
Ankush Mittal
Keyword(s):  
Video Data ◽  
Video Frame ◽  
Surveillance Video ◽  
Pixel Resolution ◽  
Data Intensive ◽  
Video Frames ◽  
Time Period ◽  
Speed Up ◽  
Hadoop Cluster ◽  
Data Intensive Applications

<p>Rapid advancement in technology and in-expensive camera has raised the necessity of monitoring systems for surveillance applications. As a result data acquired from numerous cameras deployed for surveillance is tremendous. When an event is triggered then, manually investigating such a massive data is a complex task. Thus it is essential to explore an approach that, can store massive multi-stream video data as well as, process them to find useful information. To address the challenge of storing and processing multi-stream video data, we have used Hadoop, which has grown into a leading computing model for data intensive applications. In this paper we propose a novel technique for performing post event investigation on stored surveillance video data. Our algorithm stores video data in HDFS in such a way that it efficiently identifies the location of data from HDFS based on the time of occurrence of event and perform further processing. To prove efficiency of our proposed work, we have performed event detection in the video based on the time period provided by the user. In order to estimate the performance of our approach, we evaluated the storage and processing of video data by varying (i) pixel resolution of video frame (ii) size of video data (iii) number of reducers (workers) executing the task (iv) the number of nodes in the cluster. The proposed framework efficiently achieve speed up of 5.9 for large files of 1024X1024 pixel resolution video frames thus makes it appropriate for the feasible practical deployment in any applications.</p>

scholarly journals Video Frame Interpolation via Deformable Separable Convolution

2020 ◽  
Vol 34 (07) ◽  
pp. 10607-10614 ◽  
Author(s):  
Xianhang Cheng ◽  
Zhenzhong Chen
Keyword(s):  
State Of The Art ◽  
Video Frame ◽  
Kernel Size ◽  
Frame Interpolation ◽  
Interpolation Methods ◽  
Video Frames ◽  
Convolution Process ◽  
Strong Performance ◽  
Existing Frames ◽  
Better Than

Learning to synthesize non-existing frames from the original consecutive video frames is a challenging task. Recent kernel-based interpolation methods predict pixels with a single convolution process to replace the dependency of optical flow. However, when scene motion is larger than the pre-defined kernel size, these methods yield poor results even though they take thousands of neighboring pixels into account. To solve this problem in this paper, we propose to use deformable separable convolution (DSepConv) to adaptively estimate kernels, offsets and masks to allow the network to obtain information with much fewer but more relevant pixels. In addition, we show that the kernel-based methods and conventional flow-based methods are specific instances of the proposed DSepConv. Experimental results demonstrate that our method significantly outperforms the other kernel-based interpolation methods and shows strong performance on par or even better than the state-of-the-art algorithms both qualitatively and quantitatively.

scholarly journals Object-Based Approach for Adaptive Source Coding of Surveillance Video

2019 ◽  
Vol 9 (10) ◽  
pp. 2003 ◽  
Author(s):  
Tung-Ming Pan ◽  
Kuo-Chin Fan ◽  
Yuan-Kai Wang
Keyword(s):  
Linear Model ◽  
Video Transmission ◽  
Moving Objects ◽  
Source Coding ◽  
Video Quality ◽  
Surveillance Video ◽  
Surveillance Videos ◽  
Object Based ◽  
High Recognition Accuracy ◽  
Object Dynamics

Intelligent analysis of surveillance videos over networks requires high recognition accuracy by analyzing good-quality videos that however introduce significant bandwidth requirement. Degraded video quality because of high object dynamics under wireless video transmission induces more critical issues to the success of smart video surveillance. In this paper, an object-based source coding method is proposed to preserve constant quality of video streaming over wireless networks. The inverse relationship between video quality and object dynamics (i.e., decreasing video quality due to the occurrence of large and fast-moving objects) is characterized statistically as a linear model. A regression algorithm that uses robust M-estimator statistics is proposed to construct the linear model with respect to different bitrates. The linear model is applied to predict the bitrate increment required to enhance video quality. A simulated wireless environment is set up to verify the proposed method under different wireless situations. Experiments with real surveillance videos of a variety of object dynamics are conducted to evaluate the performance of the method. Experimental results demonstrate significant improvement of streaming videos relative to both visual and quantitative aspects.

Reversible cellular automata with memory of delay type

Complexity ◽  
2014 ◽  
Vol 20 (1) ◽  
pp. 49-56 ◽  
Author(s):  
Ramón Alonso-Sanz
Keyword(s):  
Cellular Automata ◽  
Reversible Cellular Automata ◽  
With Memory

Digital motion analysis as a tool for analysing the shape and performance of the circulatory system in transparent animals

2000 ◽  
Vol 203 (11) ◽  
pp. 1659-1669 ◽  
Author(s):  
T. Schwerte ◽  
B. Pelster
Keyword(s):  
Blood Vessels ◽  
Fluorescent Probe ◽  
Motion Analysis ◽  
Vascular System ◽  
Circulatory System ◽  
Cardiac Activity ◽  
Video Frame ◽  
Peripheral Blood Flow ◽  
Gray Scale ◽  
And Performance

The analysis of perfusion parameters using the frame-to-frame technique and the observation of small blood vessels in transparent animals using video microscopy can be tedious and very difficult because of the poor contrast of the images. Injection of a fluorescent probe (fluorescein isothiocynate, FITC) bound to a high-molecular-mass dextran improved the visibility of blood vessels, but the gray-scale histogram showed blurring at the edges of the vessels. Furthermore, injection of the fluorescent probe into the ventricle of small zebrafish (Danio rerio) embryos (body mass approximately 1 mg) often resulted in reduced cardiac activity. Digital motion analysis, however, proved to be a very effective tool for analysing the shape and performance of the circulatory system in transparent animals and tissues. By subtracting the two fields of a video frame (the odd and the even frame), any movement that occurred within the 20 ms necessary for the acquisition of one field could be visualised. The length of the shifting vector generated by this subtraction, represented a direct measure of the velocity of a moving particle, i.e. an erythrocyte in the vascular system. By accumulating shifting vectors generated from several consecutive video frames, a complete trace of the routes over which erythrocytes moved could be obtained. Thus, a cast of the vascular system, except for those tiny vessels that are not entered by erythrocytes, could be obtained. Because the gray-scale value of any given pixel or any given group of pixels increased with the number of erythrocytes passing it, digital motion analysis could also be used to visualise the distribution of blood cells in transparent tissues. This method was used to describe the development of the peripheral vascular system in zebrafish larvae up to 8 days post-fertilisation. At this stage, food intake resulted in a clear redistribution of blood between muscle tissue and the gut, and alpha-adrenergic control of peripheral blood flow was established.

scholarly journals AN OPTIMIZED SURF-BASED FIRE AND SMOKE DETECTION SYSTEM USING IMAGE PROCESSING FROM SURVEILLANCE VIDEO.

2020 ◽  
Vol 9 (10) ◽  
pp. 86-96
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Feature Selection ◽  
Digital Image Processing ◽  
Digital Image ◽  
Optimization Approach ◽  
Surveillance Video ◽  
Smoke Detection ◽  
Video Frames ◽  
Optimal Feature

Over the last decades, digital image processing based fire and smoke detection have been improving steadily to provide a more accurate detection results in the area of surveillance security system. Detection of the fire and smoke from the surveillance videos is very challenging task due to the complex structural properties of the video frames or images and need improvisation in the existing work by utilization of feature selection or optimization approach to select on optimal feature according to the fire and smoke. A research based on the combination of various feature extraction techniques with feature selection approach for fire and smoke detection has been presented in this paper. In this research, we develop Fire and Smoke Detection (FSD) system using digital image processing with the concept of Speed up Robust Feature (SURF) along with the Intelligent Water Drops (IWD) as a feature selection and optimization algorithm. Here, Artificial Neural Network (ANN) is used as an Artificial Intelligence (AI) technique with that helps to select a set of optimal feature from the extracted by SURF descriptor from the video frames. By utilizing the concept of optimized ANN, the accuracy of proposed FSD system is increases in terms of detection accuracy and with minimum percentage of error. At last, the performance of the FSD system is calculated to validate the model and this shows that it is possible to use IWD with SURF as a feature extraction technique in order to detect the fire or smoke form the surveillance video with minimum error rate and the simulation results clearly show the effectiveness of proposed FSD system

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