Video Analysis with AI

Streamlining Visual Data Organization

Historically, databases have been ideal for storing and searching textual and numeric information. However, sorting videos in a way that’s efficient for the computer and accessible to humans has been a challenge. Newer methods of AI video processing changes that by offering a fast and efficient way of identifying, storing and retrieving video content.

Improving User Experience with AI

While end users are naturally skeptical of AI agents if they replace human customer service agents, there are many areas where AI can improve the user experience especially when it comes to video content. 

AI video transcription makes videos accessible to a wider audience, while tagging improves the search experience. Artificial intelligence can also improve video quality, restore or enhance low-resolution videos, and smooth out camera shakes, improving the viewing experience.

Personalized Content Recommendations Using ML

Another area where AI can improve the user experience is content discovery. Video platforms can track user viewing habits based on content tags, views and ratings, and use this to provide personalized content recommendations that are more accurate than recommendations based on simple tags.

AI in Medical Imaging

AI video analysis has led to some significant improvements in both diagnostics and patient care, as well as the field of medical research. Cutting-edge video processing techniques combined with convolutional neural networks and deep learning algorithms helps physicians detect diseases more quickly and accurately.  More and more we will see these types of technologies applied to replace or enhance old and costly image capture solutions.

Diagnostics and Detection of Medical Conditions

AI technology can interpret medical images such as X-rays, MRIs and CT scans quickly and accurately, potentially flagging up issues that might be missed by human reviewers. Using AI tools to enhance image quality and detect abnormalities can increase the likelihood of detecting diseases earlier, improving patient outcomes.

Treatment Planning Through AI Insights

Predictive medical analytics reduces the risk of misdiagnosis and helps physicians provide a higher level of patient care. Those who are flagged as “at risk” of cancer or other diseases following a scan can be sent for further tests or monitored, helping maximise the effectiveness of medical resources.

The use of AI for treatment planning goes far beyond the initial diagnosis. AI models can analyze patient data and clinical guidelines to suggest a treatment plan tailored to that patient’s circumstances. This can be beneficial if a patient has multiple diagnoses or complex risk factors that a doctor might struggle to balance.

Sophisticated algorithms can weigh potential treatment side-effects and the quality of life impact that a given treatment approach might have, based on the patient’s age, genetics, medical history and lifestyle. When combined with a patient-focused human review from a medical professional, this approach carries the potential for significant improvements in patient satisfaction and long-term health outcomes.

Automating Quality Control in Manufacturing

Computer vision algorithms can monitor production lines in real-time and detect issues, triggering an alert or an action so that a human can intervene.

AI for Product Inspection and Defect Detection

Using cameras to analyze video footage in real-time, it’s possible to identify problems such as:

•             Scratches or dents
•             Stringing on 3D printers
•             Misaligned or missing products
•             Irregularities in shape or size
•             Printing problems

Production lines are generally predictable, and it’s possible to train deep learning algorithms on footage of the line when it’s operating smoothly. Video processing software can then compare that footage with real-time footage of the production line and pick up on imperfections that a human observer might miss.

This type of automation reduces the workload faced by humans and enables manufacturers to keep their lines operating for longer, potentially at a higher pace, without sacrificing quality. AI-powered defect detection can detect subtle flaws, enabling manufacturers to take action and rectify them more promptly, reducing waste and lost time due to defective products.

Predictive Maintenance Using Anomaly Detection

If the computer detects an anomaly in the manufacturing process, this information can be sent to decision-making algorithms to trigger events such as stopping the production line or adjusting a process. It could also assist with predictive maintenance, helping maintain a higher level of efficiency in the long term.

With continuous training, deep learning algorithms such as Convolutional Neural Networks (CNNs) or Generative Adversarial Networks (GANs) can become incredibly accurate and sensitive to minor deviations from the expected performance of the line. This, combined with past information about maintenance requirements and system calibration, can help manufacturers schedule preventative maintenance at times that will have the most positive impact without sacrificing productivity.

Integrating Natural Language Processing (NLP)

Natural language processing is an important feature of machine learning and AI algorithms, and one that many people are familiar with thanks to its use in generative AI. However, it also has a place in AI video analysis.

Combining Text and Image Analysis for Multimedia Insights

Computer vision and NLP algorithms can work together in a variety of areas. For example, self-driving cars can use cameras to detect lanes and obstacles, and NLP to process text on road signs, or respond to verbal commands.

Such tools can also be useful in an educational setting. For example, computer vision could process video content and read notes, while generative AI and NLP could provide summaries or answer questions about the content.

Advanced Tools for Video Summarization

Video summarization tools have a variety of applications. Students are the most obvious demographic to whom such tools might appeal, but video summaries are also useful for researchers and busy professionals. Rather than having to sit through a long video of a meeting or conference call, an executive could request a summary and key takeaways of the video, with timestamps so they can skip to the sections that interest them the most.

Video Summarizer AI for Quick Insights

Video summarization AI tools can process lengthy videos in seconds, producing searchable transcripts and highlighting the key content in the video. Some tools can even create smart clips, so users can see key events (such as a goal in a football match, or the conclusion of a presentation), without having to manually seek and skip through the content.

AI Video Summarization for Content Optimization

One often overlooked use of AI video summary tools is content optimization. Content creators can use these tools to generate summaries to add to their YouTube (or other platform) channels, or to condense lengthy footage into a shorter format that’s better suited for the platform in question.

Smart content analysis tools might even suggest related videos to link to, further improving the value of the video and also increasing viewer retention.

Recent Solutions Delivered by Tangonet Solutions

The AI/ML experts at Tangonet Solutions have worked with several companies to develop sophisticated, efficient video analysis and computer vision systems.

Automated Traffic Monitoring and Infraction Detection

A governmental transportation agency worked with Tanognet Solutions to create a system that could automate traffic rules violation detection and reporting. The system continuously monitors traffic, detects violations in real-time, and automatically follows up on violations.

Tangonet developed a system using IP camera feeds and computer vision algorithms, with Tensorflow-Lite running on Raspberry Pi devices for edge computing. These systems were integrated with existing agency databases to ID drivers for the purposes of reporting traffic violations. 

Implementing the system helped reduce the manpower required to monitor traffic violations and also increased the accuracy of infraction reports. The transportation agency was able to reallocate human resources to other areas, further improving public safety.