Artificial Intelligence/Machine Learning can greatly help by automating traditional manual lengthy processes. If your organization is collecting bulks of video data, then one of the potential applications of AI/ML is to train models from this data.
On the surface, the process is simple. An AI engine is trained by analyzing your current stored footage, say on recognizing whether workers are wearing helmets or not. The AI engine, once trained is applied to live footage and based on its training, helps generate an alert if workers are not wearing their helmets.
But if you dig a little deeper, the process involves numerous systems interacting with each other to effectively train an AI engine and put it to good work.
Challenges with Training Machine Learning Algorithms on Bulk of Video Data
Implementing machine learning on video data and getting AI to automate a process, organizations are likely to face the following challenges for which specialized business systems need to be used.
Centralized Storage of Video Data
If your video data is being stored in disparate systems, then it’s a challenge to analyze and put them to good use. Moreover, without a centralized storage, it gets difficult to secure data, protect individual privacy and meet compliance requirements.
2. Regulatory Compliance
3. Personal/Individual Privacy
are the highest level of risks that are considered when implementing AI systems
Effective Filtering and Categorization
If you have 1000s of videos and you need to train an AI engine on identifying helmets, you need to first narrow down which videos are relevant for you to train. All of your stored videos probably won’t contain helmets in them.
The challenge lies in being able to effectively filter and narrow down to relevant videos before training an AI engine on them.
How VIDIZMO Helps in the Process?
VIDIZMO is an enterprise video content management system that helps organizations store, manage, categorize and connect video data with other systems.
With respect to machine learning, VIDIZMO helps in the following ways:
- Store videos centrally: VIDIZMO helps you store videos in a single location (deployed in the cloud or on-premise). This makes it easier to implement the necessary security controls and policies for compliance. The system can ingest live feed from multiple sources including drones, IP cameras etc.
- Categorize and filter: VIDIZMO helps in filtering video through in-built AI capabilities such as facial recognition, object detection, optical character recognition, activity detection, brand identification, topic identification and audio transcription. By narrowing down to relevant videos, you can accurately train AI engines.
- Handling video metadata: VIDIZMO can help store and manage metadata such as sensory data, video location, KLV metadata, etc. This helps in providing AI engines with extra information to train them more accurately.
- Connect with other systems: Through means of an API, VIDIZMO helps in exchanging videos and their metadata between AI engines, cameras and the video storage.
- An Interface: Once AI engines are trained, VIDIZMO provides end-users a YouTube-like portal where they can view alerts or use AI to quickly find videos.
Business Use Cases of Machine Learning on Video Data
The entire process of collecting video data, filtering, and training AI engines, has many business applications. Here are a few ones to give you an idea:
Identifying Faults in Oil Pipelines/Construction
Footage of drones surveilling oil pipelines can be stored and analyzed to identify cracks and anomalies.
Identifying Security Threats
By training AI engines to identify potential security threats, live feed from security cameras can be analyzed to promptly generate alerts.
Identifying PPE Violations
AI can be used to analyze archived camera footage for PPE to send automatic alerts whenever there is a PPE violation.
If your organization is looking to implement custom AI engines for your video data, feel free to contact us and our team would be happy to help.