Video Research Articles

Optimization of Lossless Compression Algorithms using Multithreading

The process of reducing the number of bits required to characterize data is referred to as compression. The advantages of compression include a reduction in the time taken to transfer data from one point to another, and a reduction in the cost required for the storage space and network bandwidth. There are two types of compression algorithms namely lossy compression algorithm and lossless compression algorithm. Lossy algorithms find utility in compressing audio and video signals whereas lossless algorithms are used in compressing text messages. The advent of the internet and its worldwide usage has not only raised the utility but also the storage of text, audio and video files. These multimedia files demand more storage space as compared to traditional files. This has given rise to the requirement for an efficient compression algorithm. There is a considerable improvement in the computing performance of the machines due to the advent of the multi-core processor. However, this multi-core architecture is not used by compression algorithms. This paper shows the implementation of lossless compression algorithms namely the Lempel-Ziv-Markov Algorithm, BZip2 and ZLIB algorithms using the concept of multithreading. The results obtained prove that the ZLIB algorithm proves to be more efficient in terms of the time taken to compress and decompress the text. The comparison is done for both compressions without multithreading and compression with multi-threading.

A LOW-COST SYSTEM FOR SUPPORTING DRIVERS IN TRAFFIC JAMS

Cases of heavy traffic congestion (commonly referred to as traffic jams) have negative influences on the economy, public health, and social life. This makes it important to assist drivers in these situations with Internet of Things (IoT) systems. The aim of this article is to present a low-cost device that supports driving in traffic jams. The system, which is based on data from a laser sensor with a range of up to 40 m, controls the distances to vehicles in front of and behind a moving car. Through audio and video signals, it warns the driver whether the vehicle in front of or behind the equipped vehicle is too close. This allows the driver to control the distance and avoid minor collisions.

The Methods for Improving the Quality of Detection of Inconspicuous Aerial Objects Through the Use of External Radiation Sources

The article focuses on the methods for improving the quality of detection of inconspicuous aerial objects through the use of external radiation sources. It has been established that the existing methods for improving the quality of detection of airborne objects do not meet modern requirements for detection indicators. The energy potential of modern radars is limited. Therefore, the article proposes detection methods that combine active radar and nontraditional principles for detecting airborne objects. In the article, we will focus only on two main methods for improving the quality of detection of small airborne objects. This is the use of energy from digital television receivers (external radiation source) and the use of information from ADS-B receivers. Technically, this means introducing an additional receiving channel into the radar. The gain in the conditional probability of correct detection for bistatic signal reception (compared to monostatic) is due to the properties of the bistatic effective scattering surface. It is shown that with the additional use of digital television signals, the quality of detection of airborne objects is improved. It is shown that the additional use of information from the ADS-B receivers makes it possible to increase the accuracy of determining the coordinates of an air object up to 40-50%, depending on the parameters of the movement of an air object. The subject of advanced research is the development of methods for suppressing the penetrating signal when using digital television and the development of methods for detecting airborne objects not equipped with the ADS B system. Keywords— aerial object, detection, quality, third-party source, ADS-B, digital TV, radar

A Deep Learning approach for fire object detection in Autonomous vehicles

In recent years, object detection has emerged as a crucial component of many popular consumer applications, including video surveillance and security systems, mobile text recognition, etc. The potential for autonomous vehicles (AVs) to increase driver satisfaction and decrease fatalities and injuries in traffic accidents has attracted a lot of interest in recent years. Object detection is critical to autonomous driving infrastructure. Autonomous automobiles need precise environmental interpretation to drive safely. Locating and identifying these things in real time is a significant challenge, but deep learning-based object detectors play a crucial role in this endeavour. In this paper, a prototype of the autonomous vehicle controlled by a microcontroller for fire object detection is proposed. Localization of the fire object in a picture using the deep learning model is performed using the live video feed from the camera installed on the prototype remote-operated car. The gadget may also pick up on specifics and alert the user. Experimental findings showed that the suggested prototype with deep learning architecture recognised and alerted devastating fires with high speed and accuracy in diverse weather conditions—sunny or overcast, day or night.

Estimating catch rates in real time: Development of a deep learning based Nephrops (Nephrops norvegicus) counter for demersal trawl fisheries

Demersal trawling is largely a blind process where information on catch rates and compositions is only available once the catch is taken onboard the vessel. Obtaining quantitative information on catch rates of target species while fishing can improve a fisheries economic and environmental performance as fishers would be able to use this information to make informed decisions during fishing. Despite there are real-time underwater monitoring systems developed for this purpose, the video data produced by these systems is not analyzed in near real-time. In other words, the user is expected to watch the video feed continuously to evaluate catch rates and composition. This is obviously a demanding process in which quantification of the fish counts will be of a qualitative nature. In this study, underwater footages collected using an in-trawl video recording system were processed to detect, track, and count the number of individuals of the target species, Nephrops norvegicus, entering the trawl in real-time. The detection was accomplished using a You Only Look Once v4 (YOLOv4) algorithm. Two other variants of the YOLOv4 algorithm (tiny and scaled) were included in the study to compare their effects on the accuracy of the subsequent steps and overall speed of the processing. SORT algorithm was used as the tracker and any Nephrops that cross the horizontal level at 4/5 of the frame height were counted as catch. The detection performance of the YOLOv4 model provided a mean average precision (mAP@50) value of 97.82%, which is higher than the other two variants. However, the average processing speed of the tiny model is the highest with 253.51 frames per second. A correct count rate of 80.73% was achieved by YOLOv4 when the total number of Nephrops are considered in all the test videos. In conclusion, this approach was successful in processing underwater images in real time to determine the catch rates of the target species. The approach has great potential to process multiple species simultaneously in order to provide quantitative information not only on the target species but also bycatch and unwanted species to provide a comprehensive picture of the catch composition.

Use of Spatio-Structural Parameters of the Multiscan Video Signal for Improving Accuracy of Control over Object Geometric Parameters

In the present paper, we consider the issue of improving the accuracy of measurements and the peculiar features of the measurements of the geometric parameters of objects by optoelectronic systems, based on a television multiscan in the analogue mode in scanistor enabling. It is shown that the convolution of the input signal of the rectangular profile of the light zone with the impulse response of the multiscan explains the smearing of video signal edges. The value of smearing is completely determined by impulse response and does not depend on the zone width. It is established that signals of the action from narrow light zones are smeared out substantially relative to their width, and in this case, the conventional method for determining the zone width by the video signal derivatives stops working. A method is proposed for a full reconstruction of the rectangular profile of a light zone (including a narrow one) in a developed noise-proof optoelectronic measuring device based on a multiscan with the use of spatio-structural parameters, where the width of a light zone corresponds to an extent, illumination of the zone—to brightness, and its coordinate—to centroid. It is shown that the subtraction of the dissipation of the impulse response from the video signal dissipation allows for accounting of the value of the video signal smearing out with high accuracy at the estimation of the width of a light zone, in accordance with the property of the additive accumulation of dissipation. It is established that the use of the spatio-structural video signal model permits the extension of the application of multiscan-based optoelectronic devices for the high-accuracy control and measurement of the geometric parameters of small objects.

ON-LINE VIDEO PROCESSING EMBEDDED SYSTEM FOR AUTOMOTIVE

Real-time processing for images is increasingly used in automotive industry. Video signals are nowadays used for different applications on traffic, such as measuring average speed, monitoring traffic congestion, detect red light crossing, but also for sign recognition, lane assist etc. The paper proposes an embedded system for image processing used for controlling climatization in cars. This system is also useful in case of an accident to activate the airbag if a person is on the seat or not to do it if a heavier object is placed on the seat, for example a bag. For this aim, a Raspberry Pi3 based system is proposed for real-time detecting of the number of passengers existing in a vehicle. The Raspberry Pi3 is used for acquiring frames from a video camera connected via USB interface and for on-line processing of images. The system uses an Haar cascade classifier algorithm (HCCA) for human face detection.

GRGB rPPG: An Efficient Low-Complexity Remote Photoplethysmography-Based Algorithm for Heart Rate Estimation.

Remote photoplethysmography (rPPG) is a promising contactless technology that uses videos of faces to extract health parameters, such as heart rate. Several methods for transforming red, green, and blue (RGB) video signals into rPPG signals have been introduced in the existing literature. The RGB signals represent variations in the reflected luminance from the skin surface of an individual over a given period of time. These methods attempt to find the best combination of color channels to reconstruct an rPPG signal. Usually, rPPG methods use a combination of prepossessed color channels to convert the three RGB signals to one rPPG signal that is most influenced by blood volume changes. This study examined simple yet effective methods to convert the RGB to rPPG, relying only on RGB signals without applying complex mathematical models or machine learning algorithms. A new method, GRGB rPPG, was proposed that outperformed most machine-learning-based rPPG methods and was robust to indoor lighting and participant motion. Moreover, the proposed method estimated the heart rate better than well-established rPPG methods. This paper also discusses the results and provides recommendations for further research.

Clean mucosal area detection of gastroenterologists versus artificial intelligence in small bowel capsule endoscopy.

Studies comparing the detection of clean mucosal areas in capsule endoscopy (CE) using human judgment versus artificial intelligence (AI) are rare. This study statistically analyzed gastroenterologist judgments and AI results. Three hundred CE video clips (100 patients) were prepared. Five gastroenterologists classified the video clips into 3 groups (≥75% [high], 50%-75% [middle], and < 50% [low]) according to their subjective judgment of cleanliness. Visualization scores were calculated using an AI algorithm based on the predicted visible area, and the 5 gastroenterologists' judgments and AI results were compared. The 5 gastroenterologists evaluated CE clip video quality as "high" in 10.7% to 36.7% and as "low" in 28.7% to 60.3% and 29.7% of cases, respectively. The AI evaluated CE clip video quality as "high" in 27.7% and as "low" in 29.7% of cases. Repeated-measures analysis of variance (ANOVA) revealed significant differences in the 6 evaluation indicators (5 gastroenterologists and 1 AI) (P < .001). Among the 300 judgments, 90 (30%) were consistent with 5 gastroenterologists' judgments, and 82 (91.1%) agreed with the AI judgments. The "high" and "low" judgments of the gastroenterologists and AI agreed in 95.0% and 94.9% of cases, respectively. Bonferroni's multiple comparison test showed no significant difference between 3 gastroenterologists and AI (P = .0961, P = 1.0000, and P = .0676, respectively) but a significant difference between the other 2 with AI (P < .0001). When evaluating CE images for cleanliness, the judgments of 5 gastroenterologists were relatively diverse. The AI produced a relatively universal judgment that was consistent with the gastroenterologists' judgements.

Empirical Pathloss Model Analysis of Television White Space in Lagos, Western Nigeria

To conduct feasible unlicensed communications in the television band, radio equipment must first identify transmission possibilities, or the proportion of the permitted spectrum for broadcasting services that is vacant at a given time in a particular location, which is referred to as Television White Space (TVWS). A drive test was conducted to determine the signal strength of three cell towers in three distinct areas of Lagos state (rural, suburban, and urban). The signal power parameters were measured in rural, urban, and suburban parts of Lagos in order to provide empirical values for the parameters that unlicensed radio devices can utilize to distinguish between vacant and occupied television channels in a real-world scenario. This research presents the results of field measurements in the UHF television band (470-860 MHz) conducted in Lagos. This work investigated and compared several propagation models (COST 231 model, Egli model, Okumura-Hata model, and Plain Earth model). The Standard Deviation Error (SDE), Root Mean Square Error (RMSE), and Mean Error (ME) analysis showed that the COST-231 Hata model is the optimal model for calculating path loss based on path loss exponents. This discovery led to the development of an improved model, the Oressuff TV model, which uses COST-231 Hata parameters to predict path loss in the 470–870 MHz spectrum for rural, urban, and suburban locations. From the result of Root Mean Square Error analysis, the proposed model predicted path loss in rural, urban, and suburban stations with low RMSE of 3.06dB, 3.08dB, and 1.19dB respectively. These figures indicate that the model optimization was successful, and that the Oressuff TV proposed model can estimate the path loss incurred by television signals with greater precision. Telecommunications firms may improve their service by utilizing the proposed model.

The effects of TV content on entrepreneurship: Evidence from German unification

This paper empirically analyzes whether television (TV) can influence individuals’ decisions to start businesses. To identify TV's effects, we rely on a unique quasi-natural experiment related to the division of Germany after WWII until 1990 into West Germany with a free market economy and the socialist East Germany where starting one's own business was not permitted. Despite this division, Western TV was exogeneously available since the 1960s in some, but not all East German regions and conveyed images and attitudes conducive to entrepreneurship. We use both regional-level and geo-referenced individual-level data and show that since starting a business in East Germany became possible thanks to the reunification in 1990, entrepreneurship incidence is higher in East German regions that had Western TV signal. This indicates a first-order effect on directly exposed individuals. We show that this is due to the effects of Western TV on attitudes and value orientations associated with entrepreneurship, particularly independence. We find no indication that the differences in the entrpreneurship incidence of East German regions with and without Western TV disappear. Instead, we find that successive cohorts and descendants of directly exposed individuals who were not directly exposed themselves more frequently wish to become entrepreneurs. The latter findings are consistent with second-order effects due to intergenerational transmission of an entrepreneurial mindset and suggest that a self-sustaining entrepreneurial culture can be formed. This can cause long-lasting differences between treated and non-treated population groups or regions.

Television and the Labor Supply: Evidence from the Digital Television Transition in the UK

<h3>Abstract</h3> I exploit variation in the date of transition from analogue to digital television signal in the UK across more than 40,000 geographical units to investigate the causal impact of television on employment. Using an event study model, I find that the digital transition increases employment probabilities. The impact is driven by mothers, primarily through an increase in part-time employment. I also show that the digital transition keeps children busy by increasing their TV viewing time and reduces the number of hours mothers spend on housework, as well as their reported family burdens.

Study the noise effect on the edges and cross correlation of TV images

The noise that associated with TV images is considered to be a very important problem that complicates the developments of these systems. Because of that, it makes the interpretation and analysis of informative images a very difficult process, and lessens their advantages. The most common type of these images are TV images transmitted by antenna work with (VHF, UHF) frequency and Satellite Transmission. Because TV images move through a complicated system in generating the TV or video signal, coding them, transmitting them, and receiving them by decoding and passing with different sets until appearing on screen. This will lead to a loose and fraud for a part of the information sent in the wave. As a result, it will be affected by an additive noise (Gaussian noise) which leads to deforming and destroying the quality of these images.&#x0D; This research aimed at studying the effect of the noise influence of this noise on edge as well as study of the relation between the statistical operators that include the mean and standard deviation (µ, σ) of edge’s regions for the components RGB and illumination component L as a function of time and as a function of antenna rotation.&#x0D; As well as we studied the relation between (µ, σ) and signal to noise ratio SNR for the cross correlation of edge's regions and for a homogeneous regions as well as for image as a whole once as a function of time and the other as a function of the angle of antenna rotation as in the first.

Efficient Video Watermarking Algorithm Based on Convolutional Neural Networks with Entropy-Based Information Mapper.

This paper presents a method for the transparent, robust, and highly capacitive watermarking of video signals using an information mapper. The proposed architecture is based on the use of deep neural networks to embed the watermark in the luminance channel in the YUV color space. An information mapper was used to enable the transformation of a multi-bit binary signature of varying capacitance reflecting the entropy measure of the system into a watermark embedded in the signal frame. To confirm the effectiveness of the method, tests were carried out for video frames with a resolution of 256 × 256 pixels, with a watermark capacity of 4 to 16,384 bits. Transparency metrics (SSIM and PSNR) and a robustness metric-the bit error rate (BER)-were used to assess the performance of the algorithms.

Full-Distance A-RoF Transmission Experiment Utilizing Frequency-Optimized FM Conversion

Advances in the long-distance transmission of wideband A-RoF signals are critical. The world’s longest-class transmission using a practical A-RoF system is currently just over 300 km. In this system, CATV/BS/CS video signals are frequency-multiplexed with a bandwidth of 2.1 GHz and transmitted from broadcaster buildings to houses scattered throughout the country. In recent years, there is a demand to extend this bandwidth to 3.2 GHz in order to multiplex 8K/4K ultra-high-definition video. However, A-RoF transmission over 300 km in the 3.2-GHz bandwidth is difficult and no one has been achieved. Our solution is a new transmitter that improves transmission characteristics by changing the FM center frequency and suppressing harmonics. Our experiments clarified the existence of an optimum FM center frequency that is less affected by self-phase modulation, chromatic dispersion, and residual FM signal degradation. The proposed transmitter achieved the target distance and bandwidth for the first time in the world.

시각 동기된 다중 양방향 TMDS 데이터 전송기능을 갖는 IED용 TSMB 버스 시스템의 구현 및 성능분석

In this paper, we propose a novel TSMB bus system with multiple bidirectional data transmission and time synchronization capability for IEDs(Intelligent Electronic Devices). Unlike the legacy unidirectional TMDS(Transition Minimized Differential Signaling) used for transfer digital video signals, the proposed bus system employs dual high-speed serial TMDS link for sending trigger signals from the FPGA of main processor module and receiving the sampled data from submodules such as either current or potential transformers. For simultaneous triggering multiple submdoules, we also employ the precision timing protocol processing capability in the main processor, which generates the wall clock pulse per second(PPS) synchronized to GPS. Adopting PLL for the PPS signal, we can provide the reference clock of 98.304MHz for supporting the data rate of 491.52 Mbps over each TMDS link. The data rate can support eight 24 bits ADCs for PTs or CTs in the single link, each of which requires 4800 samplings per second. An implemented TSMB bus system with FPGA consists of a single main processor with clock expander and eight submodules, in which each submodule is connected with the bidirectional TMDS link to the FPGA of main processor module. The jitter of 1 PPS between the two main processor modules was measured to be less than 102nsec at the maximum, and the jitter of the TSMB transmission data line was measured to be less than or equal to 660ps at the TP3 point of the receiving side, confirming that the TMDS specification was satisfied. Finally, the proposed time- synchronized multi-directional bus system can contribute to the improvement of the reliability and performance of the algorithm of the intelligent protection relay system through the optimal transmission of equivalent and simultaneous sampling data without the involvement of the processor.

Hand Gesture Recognition

Abstract: This work presents a computer-vision-based application for recognizing hand gestures. A live video feed is captured by a camera, and a still image is extracted from that feed with the aid of an interface. At least once per count hand gesture (one, two, three, four, and five), the system is trained. After that, the system is given a test gesture to see if it can identify it. Several algorithms that are capable of distinguishing a hand gesture were studied. It was determined that the highest rate of accuracy was achieved by using the computational neural network known as the Alexnet algorithm. Traditionally, systems have used data gloves or markers as a means of input. We are free to use the system however we like. In this way, the user can make natural hand gestures in front of the camera. The system implemented serves as an extendable basis for future work toward a fully robust hand gesture recognition system, which is still the subject of intensive research and development.

Social Distance Detector Using YOLO v3

Abstract: Due to covid nearly 27 crore people were affected by this pandemic including over 5 Lakh deaths as per WHO statistics. Covid disease is considered a pandemic when it is spread all over the world. The covid disease is being spread because of the contact of infected people with others. Therefore, to detain the spread of the virus we require an effective monitoring system that monitors people in public places. This model proposes a approach for social distancing detection using deep learning to calculate the distance between people to reduce the spread of this coronavirus. The detection tool was developed to assess a video feed and show the violations. The video captured using the camera was given as input, and the open-source object detection pre-trained model known as YOLOv3 (Transfer Learning) was used for detection because from an experimental analysis, it is proved that YOLO v3 tracking methods shows reliable results with better mAP and FPS score to detect people or objects in real-time. The camera captures the video. Using this project, we can make sure that people follow the rules of socialization.

Technology for Fundamental Improvement of an Extremely Low-Quality Video Signal for Use in Fine Focusing and Astigmatism Correction in Scanning Electron Microscopy.

This study describes important techniques for production of a series of video signals for use in fine focusing operations and near-perfect astigmatism correction in the general-purpose scanning electron microscopy (SEM) field. These techniques can enhance the stability of the signal greatly when used for focusing. As two particularly important fundamental techniques, SEM image acquisition with priority given to the signal-to-noise ratio and signal reinforcement based on the active image processing concept were utilized fully. The performance improvement was evaluated using the case of a previously reported support system for fine focusing and astigmatism correction based on image covariance. The method is almost completely robust against noise within practical limits and allows for focusing and astigmatism correction for even extremely noisy SEM images. The results of this study may be useful not only in the SEM field but also in many fields that use weak signals.

Linear LED driver design and its implementation for visible light communication applications

This study aimed to develop a linear LED driver for VLC applications which is capable of transmitting multilevel amplitude, such as OFDM formats or pure analog audio – video signals. The proposed driver comprises a general Op-Amp circuit that emits 3–12 Watts LED power. The maximum current of an Op-Amp limits the LED power. This study characterizing the I-V curves and provide insights on how to attain the LED suitable or optimal biasing point. First, the information signal undergoes clipping due to the too high bias point. Second, the LED cannot light up because the bias point is too low. Differently from the conventional Bias-T LED drivers, we exploited the readily available components such as Op-Amp that allows to work as adder device between the data signal and the DC offset. It is also a cost-effective way that uses off-the-shelf Op-Amps. Furthermore, the driver incorporates a DC-offset remover capability for an input similar to the DC signal. The experimental results showed that the linear driver’s bandwidth (BW) is ≥ 500 kHz without affecting the LED when lighting up. Moreover, the design was evaluated with BER analysis and demonstrated with a real VLC transceiver platform. The transceiver platform comprises FPGA Zync-7000, AD7302 DAC, XADC, and an off-the-shelf Analog Front-End (AFE) receiver. The proposed linear driver was clocked at 100 kHz to perform real-time VLC transmission using QPSK modulation and maximum bit-rate measurement. We have achieved a BER of < 1.5 × 10−3 and ∼30 kbps of real-time transmission speed. Therefore, it has a huge potential in IoT-based VLC networks.