Live Video Streaming Research Articles

Leveraging openCV and image processing for crime identification through facial recognition

To maintain the safety and security of communities, law enforcement agencies provide vital functions such as crime prevention and identification. Identifying people with a criminal record efficiently and accurately is one of the biggest hurdles in this field. This study delves into a fresh strategy for taking on this problem by making use of state-of-the-art image processing methods and machine learning algorithms. The project's main goal is to identify convicted felons by utilizing OpenCV, a robust open-source computer vision toolkit, and different facial recognition algorithms. By giving them a tool that guarantees a faster reaction rate and improved accuracy in suspect identification, the system is aimed to boost the capacities of law enforcement organizations. This project is based on the tenet of criminal psychology, which states that criminals are prone to committing new crimes. The system's goal is to detect and track repeat criminals both before and after they commit crimes by making use of this information. The method works by taking pictures of people who are known to be criminals and putting them in a database. From there, they can be compared to either live video streams or static photographs taken by surveillance cameras. In order to analyze and identify facial characteristics effectively, algorithms like LBPH and the Haar cascade classifier are also used. The system's robustness and efficiency are guaranteed by integrating these approaches into the OpenCV framework. This research makes a valuable contribution to the field of crime prevention by offering law enforcement authorities a dependable and scalable alternative. This system provides a streamlined interface for storing, retrieving, and matching facial photos. It was built using Python 3.5 and uses SQLite to manage the database of offenders' details. Proactively monitoring persons with a criminal record is made easier and faster with the suggested method. Suspect identification is also made easier. This research showcases the possibility of integrating machine learning and image processing to enhance public safety and crime prevention initiatives. It is an early step towards more complete video surveillance systems.

Exploring The Celebrity Effect on Live-Streaming E-Commerce: A Comprehensive SWOT Analysis of Influencer Marketing Strategies

In recent years, with the rapid development of the Internet and mobile technology, the landscape of business and marketing has changed dramatically, and particularly notable is the rise of live-streaming e-commerce. This new e-commerce model combines e-commerce with live video streaming to enhance the shopping experience through product display, promotion and sales by anchors. In this model, the celebrity effect, which takes advantage of celebrities' large fan bases and strong influence, plays an important role in driving consumer behaviour. This paper explores the impact of the celebrity effect on live-streaming e-commerce and analyses its strengths, weaknesses, opportunities and threats through SWOT analysis with typical case studies in China. The findings highlight the key role of celebrity endorsement in increasing consumer engagement and purchase intention, while also exploring the potential risks and challenges. The paper concludes with suggestions for optimisation and development to maximise the role of the celebrity effects in live-streaming e-commerce.

Point of Interest Recognition and Tracking in Aerial Video during Live Cycling Broadcasts

Road cycling races, such as the Tour de France, captivate millions of viewers globally, combining competitive sportsmanship with the promotion of regional landmarks. Traditionally, points of interest (POIs) are highlighted during broadcasts using manually created static overlays, a process that is both outdated and labor-intensive. This paper presents a novel, fully automated methodology for detecting and tracking POIs in live helicopter video streams, aiming to streamline the visualization workflow and enhance viewer engagement. Our approach integrates a saliency and Segment Anything-based technique to propose potential POI regions, which are then recognized using a keypoint matching method that requires only a few reference images. This system supports both automatic and semi-automatic operations, allowing video editors to intervene when necessary, thereby balancing automation with manual control. The proposed pipeline demonstrated high effectiveness, achieving over 75% precision and recall in POI detection, and offers two tracking solutions: a traditional MedianFlow tracker and an advanced SAM 2 tracker. While the former provides speed and simplicity, the latter delivers superior segmentation tracking, albeit with higher computational demands. Our findings suggest that this methodology significantly reduces manual workload and opens new possibilities for interactive visualizations, enhancing the live viewing experience of cycling races.

Identification and location monitoring through Live video Streaming by using blockchain

Identification and location monitoring through Live video Streaming by using blockchain

A comprehensive investigation of an LTE-enabled smart door system using the Arduino UNO

Traditional residential security systems frequently lack remote access and real-time reaction capabilities, necessitating home automation security technology developments. With the rise of the Internet of Things (IoT), solutions such as the Smart Door are prepared to transcend these constraints by leveraging modern communication technology. The article aims to develop, implement, and evaluate an LTE-enabled Smart Door system built on the Arduino Uno platform. The goal is to improve home security by offering keyless entry, real-time surveillance, and remote operation, increasing user convenience and system reliability. We created a Smart Door system with an Arduino Uno microcontroller and LTE connectivity. The system's performance was measured using a variety of measures, including response time, reliability, and security against unwanted access. Testing included simulation environments and real-world scenarios to ensure the system's operational efficacy and security. The Smart Door system performed admirably, with a reaction time of less than two seconds in 95% of commands and strong resilience to brute force attacks. It effectively incorporated keyless entry, live video streaming, and real-time alarms, all controlled by a smartphone app, demonstrating the system's dependability and efficiency in live scenarios. The LTE-enabled Smart Door system considerably improves over traditional home security options. It provides enhanced security, simple operation, and more outstanding remote management capabilities by utilizing IoT technology. These enhancements fill essential holes in existing systems, providing a safer, dependable, and convenient answer for modern home security requirements.

Usability, Ergonomics, and Educational Value of a Novel Telestration Tool for Surgical Coaching: Usability Study.

Telementoring studies found technical challenges in achieving accurate and stable annotations during live surgery using commercially available telestration software intraoperatively. To address the gap, a wireless handheld telestration device was developed to facilitate dynamic user interaction with live video streams. This study aims to find the perceived usability, ergonomics, and educational value of a first-generation handheld wireless telestration platform. A prototype was developed with four core hand-held functions: (1) free-hand annotation, (2) cursor navigation, (3) overlay and manipulation (rotation) of ghost (avatar) instrumentation, and (4) hand-held video feed navigation on a remote monitor. This device uses a proprietary augmented reality platform. Surgeons and trainees were invited to test the core functions of the platform by performing standardized tasks. Usability and ergonomics were evaluated with a validated system usability scale and a 5-point Likert scale survey, which also evaluated the perceived educational value of the device. In total, 10 people (9 surgeons and 1 senior resident; 5 male and 5 female) participated. Participants strongly agreed or agreed (SA/A) that it was easy to perform annotations (SA/A 9, 90% and neutral 0, 0%), video feed navigation (SA/A 8, 80% and neutral 1, 10%), and manipulation of ghost (avatar) instruments on the monitor (SA/A 6, 60% and neutral 3, 30%). Regarding ergonomics, 40% (4) of participants agreed or strongly agreed (neutral 4, 40%) that the device was physically comfortable to use and hold. These results are consistent with open-ended comments on the device's size and weight. The average system usability scale was 70 (SD 12.5; median 75, IQR 63-84) indicating an above average usability score. Participants responded favorably to the device's perceived educational value, particularly for postoperative coaching (agree 6, 60%, strongly agree 4, 40%). This study presents the preliminary usability results of a novel first-generation telestration tool customized for use in surgical coaching. Favorable usability and perceived educational value were reported. Future iterations of the device should focus on incorporating user feedback and additional studies should be conducted to evaluate its effectiveness for improving surgical education. Ultimately, such tools can be incorporated into pedagogical models of surgical coaching to optimize feedback and training.

Reform of Photography Education in Chinese Applied Universities in the Self-Media Era: Evidence from Responsible Persons in the Photography Major

Live streaming and short videos have become the most favored means of communication, and the prevalence of self-media has had a profound impact on traditional media industries, indirectly promoting reforms in Chinese photography education. This study delves into the current state of photography education in Chinese applied universities and the transformations undertaken in response to the challenges posed by self-media. The research covers the development status of university photography programs, the impact of live streaming and short videos on photography education, changes in teaching models, and the issues encountered during these reforms. Through in-depth interviews with heads and instructors of photography programs at five applied universities in China, and using NVIVO software for thematic analysis, the study examines the professional divisions, curriculum design, teaching methods, student employment rates, reform policies, and the challenges faced. The research finds that the prevalence of live streaming and short videos has had a positive impact on the dynamic imaging specialties within photography education, but has been detrimental to the static imaging specialties. Universities have actively reformed their teaching methods in response to market demands, yet still face challenges in integrating live streaming-related courses. This study provides the latest insights for practitioners in the Chinese self-media industry and offers valuable references for university policymakers and researchers.

Digital Twin-Based Alternate Ego Modeling and Simulation: Eva Herzigová as a 3D MetaHuman Avatar

Abstract Due to Dimension Studios and Unsigned Group, by likeness and movement digitization, the human supermodel Eva Herzigová’s highly realistic metahuman and 3D digital twin lead to perpetual career extension (e.g., on virtual runway shows by catwalk replication or in advertising campaigns by particular pose adoption), including in virtual worlds such as metaverse spaces, by use of extended reality technologies, in terms of virtual clothing, digital hair, skin texture, facial and body expressions, and makeup styling as avatar appearance and persona. In this paper, we show how, by likeness capturing (e.g., signature walk by motion capture shoot for face and body movement tracking) and virtual clothing, Eva Herzigová’s hyperreal 3D avatar (lifelike 3D digital human clone or realistic virtual human) is configured with subsequent restyled hair and make-up, 3D clothing modeling, and extended reality fashion shows on virtual catwalks or campaign shoots. We clarify that photographic reference materials and a sensor-based motion capture suit assisted Metahuman Creator in capturing facial and motion data for Eva Herzigová’s digital avatar, configuring realistic depiction and virtual activations of beauty standards, in addition to digital likeness, signature style, talent representation, and narrations, typifying digital human creation and presence in virtual worlds (e.g., for metaverse-based shoots). We conclude that Eva Herzigová’s hyperrealistic 3D digital human twin and MetaHuman avatar operate as a styled virtual alter ego and versatile personal brand extension by motion capture technologies for immersive virtual fashion shows, live video streams and broadcast, and digital wearable modeling.

A Lightweight Visual Understanding System for Enhanced Assistance to the Visually Impaired Using an Embedded Platform

Visually impaired individuals often face significant challenges in navigating their environments due to limited access to visual information. To address this issue, a portable, cost-effective assistive tool is proposed to operate on a low-power embedded system such as the Jetson Nano. The novelty of this research lies in developing an efficient, lightweight video captioning model within constrained resources to ensure its compatibility with embedded platforms. This research aims to enhance the autonomy and accessibility of visually impaired people by providing audio descriptions of their surroundings through the processing of live-streaming videos. The proposed system utilizes two distinct lightweight deep learning modules: an object detection module based on the state-of-the-art YOLOv7 model, and a video captioning module that utilizes both the Video Swin Transformer and 2D-CNN for feature extraction, along with the Transformer network for caption generation. The goal of the object detection module is for providing real-time multiple object identification in the surrounding environment of the blind while the video captioning module is to provide detailed descriptions of the entire visual scenes and activities including objects, actions, and relationships between them. The user interacts via a headphone with the proposed system using a specific audio command to trigger the corresponding module even object detection or video captioning and receiving an audio description output for the visual contents. The system demonstrates satisfactory results, achieving inference speeds between 0.11 to 1.1 seconds for object detection and 0.91 to 1.85 seconds for video captioning, evaluated through both quantitative metrics and subjective assessments.

Video stalling identification for web live streaming under HTTP-FLV

Live broadcasts have become one of the most popular forms of entertainment. Quality of user Experience (QoE) is a vital quantitative criterion for evaluating user satisfaction while watching live broadcasts, and it is positively correlated with the increase in the income of Internet Service Providers (ISPs). Video stalling identification plays a crucial role in the evaluation of QoE. However, encrypted live streaming hides video content, which makes identifying video stalling challenging. Existing studies primarily detect video stalling in a fixed time interval and focus on high-dimensional features. However, the capacity of the client byte buffer is dynamic, resulting in the stalling and non-stalling existing in a certain and fixed stalling time. In addition, the handling time of abundant features causes further latency. We propose Truncation of Dynamic Bytes and non-linear Integrated Modification based on Double Buffers (DB2) to identify video stalling under HTTP-FLV protocol in various network conditions and live types. We pull real-time video to get client buffer parameters and build a dynamic mapping based on the double buffer between network packets and the video playing states. This allows a more objective and precise evaluation of video stalling. We design a new network feature by creating a non-linear relationship between network packets and the client buffer. This is achieved by combining non-linear convergent distribution with basic traffic features. The feature is fed into a lightweight machine learning model to train the classifier, achieving low processing latency and high identification accuracy. The experimental results show that DB2 can achieve 98.91% stalling identification accuracy with 1.256 ms operation time in a mixture of live video types, outperforming state-of-the-art techniques.

Leveraging Edge Computing for Video Data Streaming in UAV-Based Emergency Response Systems.

The rapid advancement of technology has greatly expanded the capabilities of unmanned aerial vehicles (UAVs) in wireless communication and edge computing domains. The primary objective of UAVs is the seamless transfer of video data streams to emergency responders. However, live video data streaming is inherently latency dependent, wherein the value of the video frames diminishes with any delay in the stream. This becomes particularly critical during emergencies, where live video streaming provides vital information about the current conditions. Edge computing seeks to address this latency issue in live video streaming by bringing computing resources closer to users. Nonetheless, the mobile nature of UAVs necessitates additional trajectory supervision alongside the management of computation and networking resources. Consequently, efficient system optimization is required to maximize the overall effectiveness of the collaborative system with limited UAV resources. This study explores a scenario where multiple UAVs collaborate with end users and edge servers to establish an emergency response system. The proposed idea takes a comprehensive approach by considering the entire emergency response system from the incident site to video distribution at the user level. It includes an adaptive resource management strategy, leveraging deep reinforcement learning by simultaneously addressing video streaming latency, UAV and user mobility factors, and varied bandwidth resources.

The influence of live video hosts' speech act on purchase behaviour

With the broad adoption of internet, China's e-commerce industry has grown vigorously. The influence mechanism between the host's speech act and the consumer's purchase behaviour deserves further exploration. Based on speech act theory, this study divided the speech acts into four categories: representative, expressive, commissive and directive. We established a model investing the effect of different speech acts on customer engagement and purchase behaviour. We recorded live streaming video and transferred the voice to the text. Our research shows that the representative act and the expressive act negatively affect purchase behaviour, whereas the commissive act has a positive impact on purchase behaviour. Furthermore, customer engagement mediates the negative impact of the directive act and the representative act on purchase behaviour. The conclusions can provide guidance for enhancing the speech skills of the hosts in live streaming.

Face Recognition based Attendance System

In today’s era, facial recognition emerges as a pivotal tool, offering security, authentication, and identification benefits across various sectors. This paper introduces an automated attendance system that incorporates real-time face recognition technology. The system aims to overcome the limitations and inefficiencies associated with traditional manual attendance methods, particularly in educational institutions and workplace environments. Two approaches are outlined: one utilizing local servers and AWS cloud recognition API, and the other relying solely on AWS resources for processing. Both approaches entail constructing a database of individuals’ images and utilizing algorithms such as Haar-Cascade and Local Binary Pattern Histogram for face detection and recognition. By automating attendance through live video streams, the system mitigates issues like proxy attendance and inaccuracies. Technologies such as OpenCV, dlib, and MySQL are leveraged to streamline implementation. This integration of face recognition with cloud computing promises to revolutionize attendance tracking, offering scalability, efficiency and accountability.

Joint Optimization for Quality Selection and Resource Allocation of Live Video Streaming in Internet of Vehicles

Joint Optimization for Quality Selection and Resource Allocation of Live Video Streaming in Internet of Vehicles

The Effect of Live Video Streaming and e-WOM on Purchasing Decisions of Tik Tok Fashion Consumers with Trust as a Mediating Variable in Pekanbaru

The Effect of Live Video Streaming and e-WOM on Purchasing Decisions of Tik Tok Fashion Consumers with Trust as a Mediating Variable in Pekanbaru

CA-Live360: Crowd-assisted transcoding and delivery for live 360-degree video streaming

In the burgeoning field of digital media, the popularity of live 360-degree video has witnessed an upward trajectory, underpinned by its immersive experience. In live 360-degree video streaming services, transcoding enormous video tiles and providing low-latency services for global viewers require significantly more computing and communication resources than traditional live services. Inspired by the huge crowdsourcing computational resources available at crowd, this paper designs CA-Live360, a Crowd-Assisted Live 360-degree video streaming solution, in which transcoding and delivery are supported by crowdsourcing devices. Due to the dynamic availability of resources, it is challenging to achieve low-latency live services, while guaranteeing fair transcoding. Therefore, firstly an innovative fairness-guaranteed transcoding task assignment scheme is proposed. This scheme employs a novel Fair Bandit algorithm based on the combinatorial multi-armed bandit approach to achieve low-latency transcoding while considering time-varying available resources and fairness constraints. Secondly, to reduce delivery latency, we design a transcoding-aware delivery scheme, in which a provider–requester matching algorithm is introduced to realize effective requester scheduling. Real-world trace-driven experiments demonstrate the improved performance of the proposed CA-Live360 solution in terms of system delay and fairness.

An Effective Investigation for Quality of Service Enhancement of Content Delivery Network for HTTP Live Streaming Using H.265

In the contemporary era, the prevailing trend among individuals is the widespread utilization of streaming technology to indulge in audio or video content emanating from a server. This innovative approach negates the necessity for downloading the content beforehand, allowing users to seamlessly revel in the material through the streaming mechanism. However, this cutting-edge technology mandates a substantial bandwidth to uphold its Quality of Service (QoS) at an optimal level. Challenges may arise, such as packet loss, attributed to the restricted bandwidth provided by the network service provider, thereby compromising the delivered content’s quality. To address this issue, one viable solution is the integration of CDN (Content Delivery Network), a technology proven to enhance the quality of content delivered through streaming services. This research endeavors to elevate the quality of live video streaming by leveraging CDN. The HTTP Live Streaming (HLS) format for content streaming, the study empirically demonstrates that CDN significantly improves the Quality of Service. As a result, the proposed method Content Delivery Network (CDN) using H.265 plays vital role in live video streaming. The result shows that live video streaming with CDN outperforms without CDN. Specifically, the live video streaming experience with CDN showcases a remarkable throughput average of 5899.7 kbps, coupled with a minimal packet loss ratio average of 0.05%. This stands in stark contrast to the version without CDN, which exhibits a comparatively diminished throughput average of 5287.3 kbps and a higher packet loss ratio average of 0.22%. These empirical findings strongly emphasize the effectiveness of CDN in elevating the Quality of Service for live video streaming. The tangible enhancements in throughput and the reduction of packet loss achieved through CDN implementationcontribute significantly to an enriched user experience. The evidence supports the notion that integrating CDN technology not only optimizes the technical performance of live video streaming but also plays a pivotal role in enhancing the overall satisfaction and engagement of users with the content delivery system.

Video livestreaming from medical emergency callers’ smartphones to emergency medical dispatch centres: a scoping review of current uses, opportunities, and challenges

BackgroundTimely dispatch of appropriate emergency medical services (EMS) resources to the scene of medical incidents, and/or provision of treatment at the scene by bystanders and medical emergency lay callers (referred to as ‘callers’ in this review) can improve patient outcomes. Currently, in dispatch systems worldwide, prioritisation of dispatch relies mostly on verbal telephone information from callers, but advances in mobile phone technology provide means for sharing video footage. This scoping review aimed to map and identify current uses, opportunities, and challenges for using video livestreaming from callers’ smartphones to emergency medical dispatch centres.MethodsA scoping review of relevant published literature between 2007 and 2023 in the English language, searched within MEDLINE; CINAHL and PsycINFO, was descriptively synthesised, adhering to the PRISMA extension for scoping reviews.ResultsTwenty-four articles remained from the initial search of 1,565 articles. Most studies were simulation-based and focused on emergency medical dispatchers’ (referred to as ‘dispatcher/s’ in this review) assisted video cardiopulmonary resuscitation (CPR), predominantly concerned with measuring how video impacts CPR performance. Nine studies were based on real-life practice. Few studies specifically explored experiences of dispatchers or callers. Only three articles explored the impact that using video had on the dispatch of resources. Opportunities offered by video livestreaming included it being: perceived to be useful; easy to use; reassuring for both dispatchers and callers; and informing dispatcher decision-making. Challenges included the potential emotional impact for dispatchers and callers. There were also concerns about potential misuse of video, although there was no evidence that this was occurring. Evidence suggests a need for appropriate training of dispatchers and video-specific dispatch protocols.ConclusionResearch is sparse in the context of video livestreaming. Few studies have focussed on the use of video livestreaming outside CPR provision, such as for trauma incidents, which are by their nature time-critical where visual information may offer significant benefit. Further investigation into acceptability and experience of the use of video livestreaming is warranted, to understand the potential psychological impact on dispatchers and callers.

Optimizing live video streaming: Integrating 5G, IoT, and cloud computing with machine learning

AbstractIn this research, we optimize live video broadcast performance by incorporating advanced technologies such as 5G, the Internet of Things (IoT), and cloud computing. Our approach utilizes the Random Forest classifier to categorize data, achieving a 99% precision rate. A comparative study demonstrates that our proposed technique outperforms RCNN and Mask‐RCNN methods in optimizing video streaming efficacy. We show that our method efficiently enhances video streaming quality by integrating machine learning technologies. The combination of 5G, IoT, and cloud computing creates a robust environment for delivering optimized Live video streaming to users. This research underscores the importance of leveraging cutting‐edge technology to address optimization challenges in modern video streaming systems, focusing on the real‐time optimization of video streams in contemporary technological environments.

Real-Time Object Detection and Recognition with Computer Vision

Abstract: Object detection, a fundamental aspect of computer vision, is essential for identifying and localizing objects within images or video frames, leveraging advancements in deep learning, particularly convolutional neural networks (CNNs), to enhance precision and speed. Its applications span diverse domains, from autonomous vehicles and surveillance systems to augmented reality and human-computer interaction. Our project focuses on engineering a real-time object detection system, integrating deep learning and computer vision methodologies. Anchored on the robust Single Shot Multibox Detector (SSD) architecture and reinforced by the efficiency and accuracy of the MobileNetV3 backbone, our system utilizes a pre-trained SSD MobileNetV3 model and comprehensive annotations from the COCO dataset to adeptly detect and recognize a wide array of objects within live video streams or archived footage. It seamlessly processes video frames from various sources, annotating detected objects in real-time to provide instant visual feedback. Offering customizable confidence thresholds and support for multiple video sources, our project showcases the transformative potential of deep learning and computer vision, advancing realtime object detection across domains like surveillance and interactive systems. By pushing the boundaries of object detection technology, our project aims to enhance safety, efficiency, and user experiences in various applications, promising to redefine the landscape of computer vision with innovation and advancement.