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Real-Time Video Streaming Applications | Complete Overview

Dr. Jagreet Kaur Gill | 07 December 2023

Real-Time Video Streaming Applications

Introduction to Real-Time Video Streaming

In today's digital era, video has become one of the most prevalent and influential forms of media for transmitting information. With the exponential growth of video data, there is an increasing need for efficient and effective methods to analyze and extract valuable insights from these vast streams of visual content. This is where real-time video artificial intelligence (AI) applications come into play, revolutionising industries.

Deep dive into Real-Time Video Analytics with Generative AI

Real-time video AI applications rely on computerized processing and automatic video content analysis to detect and determine temporal and spatial events. By leveraging advanced computer vision techniques and machine learning algorithms, these applications enable the extraction of meaningful information from unstructured video data in real-time, empowering organizations to make data-driven decisions and gain a competitive edge. Real-time video emerged as a game-changer, enabling organizations to unlock the full potential of their video data. The traditional approach of manually reviewing and analyzing hours of video footage is time-consuming and prone to human error. Real-time video AI applications address these challenges by automating the analysis process and providing instant insights.

A Video Analytics Engine with Artificial Intelligence is a Platform to utilises Advanced Image Processing Algorithms to turn video into Actionable Intelligence. Taken From Article, Cloud Video Analytics and Surveillance for Intelligent Outcomes

What is Real-Time Video Streaming?

Real-time video streaming is a technology that allows users to transmit and receive video data in near real-time over the internet. It enables seamless and instantaneous visual communication, empowering interactive experiences across different domains. This technology relies on robust network infrastructure, efficient video compression techniques, and streaming protocols to ensure smooth and uninterrupted video transmission.

Real-time video streaming has significantly impacted various industries and applications. It has revolutionised e-commerce by enabling live product demonstrations and virtual try-on experiences. It facilitates remote consultations and telemedicine, improving access to medical services in healthcare. Security and surveillance systems utilize real-time video streaming for monitoring and incident response. Social media platforms and video-sharing websites allow individuals to share their experiences with a global audience in real-time. The future of real-time video streaming looks promising with the integration of technologies like augmented reality (AR) and virtual reality (VR) into live video streams. Additionally, machine learning and AI advancements enhance video analytics capabilities, enabling real-time content analysis for tasks like object recognition and sentiment analysis, connecting people across distances, and enabling immersive experiences. Its applications continue to expand, and with ongoing technological advancements, it holds the potential to enrich our digital lives further.

Know more about Intelligent Video Analytics for the Entertainment Industry

What is Video File Splitting?

Video splitting is a technique used in video processing to divide a video into multiple parts and process them simultaneously. This method aims to improve efficiency and leverage parallel processing capabilities. By splitting the video, algorithms can work concurrently, utilizing slower, more accurate models.

In video splitting, the splitting process is virtual rather than physical sub-file generation. It involves dividing the video into segments or parts that can be processed independently. This approach is commonly implemented using video time pointers to mark the boundaries of each segment. However, it is essential to note that the output of the splitting process results in multiple video files, which need to be combined into a single video file afterward. An overlap is often introduced during the splitting process to ensure continuity and avoid missing information at joint places.

Although video file splitting can enhance processing efficiency, there are better options for real-time video processing tasks. One limitation is the difficulty in pausing, resuming, or rewinding a video while it is being processed. Managing these operations becomes challenging since the video is split into separate parts. Additionally, file splitting is more suitable for offline video processing scenarios, where the entire video can be processed as separate segments without real-time interaction or manipulation.

Despite its limitations, video file splitting remains valuable for specific video processing tasks. It can be effective for batch processing of pre-recorded videos, where the entire video can be split and processed independently. However, alternative approaches, such as the pipeline architecture, may be more appropriate for real-time video processing applications that require interactive capabilities and on-the-fly adjustments.

To analyze the performance of their particular episode of a particular series through real-time data. Taken From Article, Intelligent Video Analytics for Entertainment Industry

What is the role of AI in Real-Time Video Analysis?

Artificial Intelligence (AI) plays a crucial role in real-time video analysis, revolutionizing how we extract valuable insights and make sense of vast amounts of visual data. Here are some critical roles of AI in real-time video analysis.

1. Object Detection and Tracking

Artificial Intelligence enables real-time object detection and tracking in video streams. By employing computer vision techniques and deep learning algorithms, AI systems can accurately identify and track objects of interest, such as people, vehicles, and other entities. This role of AI is crucial in video surveillance, autonomous vehicles, and visual monitoring systems.

2. Action Recognition

AI plays a significant role in real-time action recognition from video data. AI algorithms can recognize and classify human actions and activities by analyzing motion patterns and body movements. This capability has applications in various domains, including video surveillance, sports analysis, and healthcare monitoring.

3. Anomaly Detection  

Artificial Intelligence detects anomalies and unusual events in real-time video streams. By learning patterns from normal behavior, AI models can identify deviations and raise alerts when unexpected events occur. Anomaly detection is vital for security systems, critical infrastructure monitoring, and proactive surveillance.

4. Facial Recognition

AI-based facial recognition technology enables real-time identification and verification of individuals based on facial features. AI systems can accurately recognize individuals in video streams by analyzing facial patterns and comparing them against a database. Facial recognition has applications in law enforcement, access control, and personalized user experiences.

5. Video Summarization  

Artificial Intelligence facilitates real-time video summarization by automatically extracting key frames or remarkable events from long video sequences. This enables users to quickly grasp the content of lengthy videos without requiring manual review. Video summarisation is beneficial in fields such as surveillance, news reporting, and content moderation.

These five roles of Artificial Intelligence in real-time video analysis demonstrate its transformative impact on extracting insights, enhancing security, and improving decision-making from vast amounts of visual data.

Real-Time Video AI Systems Architecture and Workflow

To implement the pipeline approach in the application, several techniques and optimisations can be employed


The architecture of Real-Time Video AI Systems is described below:

Interpolation with Tracking

  1. Bounding boxes can be interpolated using a tracking algorithm to skip computations for specific frames.
  2. Simple algorithms like centroid distance matching or more complex tracking algorithms can be used.
  3. Evaluating the quality of interpolated frames, such as calculating the F1 metric, ensures accurate results.

Sharing Memory

  1. Efficient interprocess communication requires sharing memory between processes.
  2. Methods like Torch.multiprocessing, Linux/dev/shm, and Python 3.8 SharedMemory can be used.
  3. System-level shared memory approach, using a particular folder as a link to RAM, enhances communication speed.
  4. Python 3.8's shared memory API is also a viable option.

Multiple Workers

  1. Adding multiple workers for a pipeline component improves performance, but careful consideration is required.
  2. Most effective for computationally heavy operations that don't require ordered input.
  3. Adding more workers may only sometimes improve FPS for tasks like face detection due to process management overhead.
  4. Proper CUDA stream management is essential when using neural networks with multiple workers.
  5. Ensuring the correct order of frames throughout the pipeline stages, especially in encoding, may require additional effort.

Workflow of Real-Time Video AI Systems 

Data Input

1. Real-time video streams are fed into the pipeline for analysis.
2. Frame-by-frame processing is performed to extract meaningful information.


Frames may undergo preprocessing steps such as resizing, normalisation, or denoising. 

Preprocessing improves the quality of input frames and enhances subsequent analysis.

Video Analysis

1. Video frames are analyzed using AI algorithms for object detection, tracking, or activity recognition.
2. Techniques like interpolation with tracking and multiple workers are employed to improve efficiency and performance.


1. Results from video analysis are processed to generate desired outputs.
2. Postprocessing may involve tasks like filtering, aggregation, or visualisation.

Output and Visualisation

1. The results are presented or visualized in a suitable format.
2. This could include visual overlays, data representations, or real-time alerts.

By implementing the suggested architecture techniques and following the workflow, real-time video analysis systems can efficiently process and analyze video streams, enabling accurate and timely insights from the data.

Video Analytics with Deep Learning

Deep learning, particularly Deep Neural Networks (DNNs), has revolutionized video analytics by enabling systems to mimic human behavior and perform complex tasks. Previously, video analytics relied on traditional computer vision techniques for simple tasks like detecting image changes. However, with deep learning, systems can now identify objects and track their movement in images.

For example, Optical Character Recognition (OCR) has been used to extract text from images, but running OCR on traffic camera images is unreliable. Deep learning models can accurately locate specific regions within an image, such as license plates, allowing OCR to be applied only to those regions for more reliable results. The integration of deep learning in video analytics has significantly enhanced system capabilities. Deep neural networks enable tasks like object detection, tracking, and region-specific analysis, improving accuracy and efficiency in real-world applications.

What are the Real-time Video AI Applications?

 The applications of Real-time Video AI are described below:


Evolution of video streaming and analytics in healthcare

1. Initially focused on surveillance for safety purposes.
2. The application has expanded to encompass broader objectives, including data analysis for achieving business goals.

Improved patient care and operational efficiency

1. Video analytics enables the detection of neglected patients, optimizing patient care. 
2. Reduces wait times and ensures efficient access to emergency areas.
3. At-home monitoring for fall detection and medication adherence.

Mental Healthcare Applications

1. Video analytics assists clinicians in analyzing facial expressions, body posture, and gaze. 
2. Provides objective information to support evaluations and diagnoses.

Real-world application: Autism Spectrum Disorder (ASD) detection

1. The smartphone app utilizes facial expressions and gaze tracking.
2. Accurately detects ASD in children.

Overall benefits of video analytics in healthcare

1. Contributes to safety, patient care, and operational improvements.
2. Leverages surveillance data for comprehensive analysis.

Smart Cities / Transportation

Enhancing traffic management and Safety in smart cities

1. Real-time video streaming and analytics are valuable tools in developing smart cities, particularly transportation.
2. Intelligent video analysis solutions improve traffic management and safety.

Dynamic adjustment of traffic lights and monitoring of congestion

1. Video analytics systems enable the dynamic adjustment of traffic lights based on real-time traffic conditions. 
2. Traffic congestion can be monitored and managed more effectively.

Real-time detection of hazardous situations

1. Video analytics solutions can detect hazardous situations such as unauthorised parking, wrong-way driving, erratic vehicle movement, and accidents.
2. Enables prompt response and intervention to ensure safety on the roads.

Valuable statistics and insights for Traffic optimization

1. Data collected by video analytics platforms provides valuable statistics and insights.
2. It helps understand traffic patterns, control driver behaviour, and optimize traffic flow.


Understanding and catering to customers in retail

1. Real-time video streaming, analytics, and machine learning have revolutionized retail.
2. Algorithms can recognize faces, determine critical characteristics, and track customer journeys through stores.

Optimizing store organization and product placement

1. Video analytics data enables retailers to optimize store layouts and product placement. 
2. It helps maximize sales and enhances the overall customer experience.

Actionable insights for retailers

1. Video analytics solutions provide valuable insights such as customer count, demographics, visit duration, and walking patterns.
2. It helps retailers gain a deep understanding of their customer base and behavior.

Anti-theft measures and security

3. Video analytics aids in anti-theft measures by employing face recognition algorithms.
4. Identifies known shoplifters or detects suspicious activities in real time.

Integration with machine learning for improved operations

1. Information extracted from video analytics serves as input for machine learning models.
2. Enhances demand forecasting, price optimization, and inventory management.

Real-world example: Amazon Go

1. Amazon Go utilizes accurate video analysis software combined with sensors.
2. Tracks customer behavior and enables seamless checkout-less shopping experiences.


Valuable insights for coaches and team performance evaluation

1. Data and video analytics provide statistics like ball possession and pass counts.
2. Coaches use this information to evaluate team performance and make strategic decisions.

Understanding strategies and winning rates

1. Studies have shown a correlation between ball possession and match outcomes.
2. Video analytics helps in understanding strategies and improving win rates.

Technique improvement and injury prevention

1. Video analytics solutions analyze athletes' poses to improve technique.
2. Identify risky movements and help prevent injuries.

Developing effective counterstrategies

1. Video analytics aids in understanding opponents' gameplay.
2. It helps teams develop effective counterstrategies and exploit weaknesses.

Data-driven Approaches in Soccer

1. Soccer teams in the UK have embraced data-driven approaches.
2. Hire experts to analyze and leverage data for player recruitment, tactical decisions, and performance tracking.

Transformation of decision-making processes

1. The emphasis on data has transformed soccer teams' decision-making processes. 
Data analysis is vital in player recruitment, tactics, and performance evaluation.
2. The use of data and video analytics has become integral in the world of soccer. 
Data-driven approaches have elevated the importance of data in sports.
Using Artificial Intelligence and video surveillance to reduce road incidents and make the road safer. Taken From Article, Illegal Parking Detection Using Video Surveillance

Streamline Video Production Process

Artificial Intelligence (AI) transforms the video production process by automating various steps and improving efficiency. Here are the key points:

  1. AI automates editing by analyzing content and generating engaging scripts, saving time and effort.
  2. AI can automate recording footage and directing actors, increasing efficiency and accuracy.
  3. AI-powered editing automates transforming raw footage into a polished final product, saving time and enhancing video quality.
  4. AI algorithms improve video quality by adjusting lighting, contrast, and color levels and reducing noise.
  5. AI suggests edits based on content analysis, ensuring optimal sound levels and enhancing the final product.
  6. AI assists in rendering and compressing videos, focusing on essential elements, and improving rendering times and quality, even for 4K videos.
  7. AI combined with cloud technology enables easy access to state-of-the-art video production tools without significant costs or equipment requirements.
  8. AI can detect similar video scenes, allowing users to locate content efficiently and discover related videos.
  9. Automatic speech recognition (ASR) technology enables accurate audio transcription, including accents or dialects, and can be trained to detect voice emotion.
  10. ASR systems have applications in translation, customer service, and personalized advertising campaigns based on voice analysis.

AI streamlines the video production process by automating tasks, improving video quality, and enhancing efficiency, making it more accessible to content creators.

What are the Challenges of Real-Time Video AI?

The main three challenges in building real-time video AI applications are:

Data Quality and Availability  

Acquiring and curating high-quality and diverse datasets that accurately represent real-world scenarios is crucial for training and inference in video AI applications. Obtaining labeled data can be challenging, and ensuring data quality and diversity is essential for developing effective models.

Computational Requirements  

Real-time video AI applications often involve complex algorithms and models that require significant computational resources. Processing high-resolution video in real-time can be computationally intensive, necessitating robust hardware infrastructure or cloud-based solutions to handle the workload efficiently.

Real-time Processing Constraints

Achieving real-time performance requires efficient algorithms and optimized implementations. The processing time should be fast enough to keep up with the video input rate, ensuring timely and responsive results. Balancing accuracy and speed is crucial to meet real-time requirements and deliver actionable insights promptly.


Building real-time video AI applications offers significant benefits in various domains, such as sports, security, and video production. With advancements in AI technology, tasks like analyzing video content, detecting objects or events, and automating editing processes have become more efficient and accurate.

Real-time video AI applications have the potential to enhance sports performance analysis, improve security systems through facial recognition and crowd management, and streamline the video production process by automating scripting, recording, and editing.

Additionally, AI algorithms improve video quality by adjusting lighting, reducing noise, and suggesting edits. The combination of AI and cloud computing enables more accessible access to video production tools without extensive investments in equipment. Real-time video AI applications promise to transform various industries and enhance the overall user experience.