Video Annotation: Enhancing the Video Surveillance and Security Systems

Artificial intelligence (AI) has impacted the video surveillance and security industry in a big way. It has automated threat and theft detection and transformed the industry through real-time monitoring of video footage, smart analysis of possible security threats and steps that can be taken to prevent them.

CCTV monitoring is reliant on human supervision. However, in most cases humans are unable to pick up important security clues leading to horrible outcomes for organizations. Another reason for this lapse is due to eye fatigue caused as a result of constant monitoring of video footages. AI benefits the surveillance and security ecosystem as machines are free from human complacency, hence, they are always accurate. It ensures surveillance and security of an organization by not missing out on any detail, information or threats.

Significance of Video Annotation in the Surveillance and Security Ecosystem

Video frames need to be graded with labeling scores to determine whether the models are able to correctly distinguish between benign and threatening subjects. Most large IT companies have brought about IT solutions for detecting unattended objects like bags in airports and public spaces, hence enabling security agencies to take prompt security measures.

Video annotation is akin to image annotation as it teaches computers to discern objects in videos. Both these techniques are part of computer vision and used for enabling computers with the human eyes’ capability to perceive. Both human and automated video annotation tools involve adding labels to objects in a video clip. Thereafter, AI and machine learning is used by computers to process the labels and identify similar objects in other videos which are not labeled.

The video annotation process enables AI to perform the following:

1. Object detection: AI models can be trained to identify and locate objects (people, animals, other vehicles, etc.) in images or videos.

2. Person identification: AI models can be trained to identify people in images or videos on the basis of their facial features, gait, and other key traits.

3. Behavioral analysis: AI models can be trained to interpret and understand suspicious behavior (loitering or tailgating) of people in images or videos.

4. Detecting threats: AI models can identify possible threats in images or videos. It can also be trained to detect threats like weapons, explosives and other suspicious activity.

5. Detecting fraud: AI algorithms require training to point out fraudulent activities like credit card fraud or insurance fraud. This is done by analyzing data like purchase history, transaction patterns and social media activity.

Use Cases for Image and Video Annotation for Surveillance

1. Tracking pedestrians: This type of labeling is done for applications dealing with foot traffic monitoring, traffic light control, or jaywalking detection. Bounding box annotation is done with interpolation through frames. It also involves labeling the state of the person per frame or object.

2. Smart offices and buildings: Datasets consisting of bounding boxes and action labels are apt for companies who wish to introduce smart office applications to make their office environment energy efficient and interactive.

3. Monitoring events: AI can be used in big events like festivals or football games for estimating correct headcount and action monitoring for big crowds. It offers useful insights regarding attendee flows for optimization and also assists in identifying risks in real-time with human-in-the-loop.

4. Detecting weapons: This is key in public areas and institutions like schools. It also benefits from a human-in-the-loop approach. The key to success lies in a comprehensive real life and synthetic datasets of situations in monitored spaces coupled with real-time monitoring by human operators.

5. Monitoring patients: Ensuring safety of patients and elderly at homes, hospitals, etc is paramount as even a minor undetected fall on the floor can have serious health repercussions. This particular use case needs properly annotated training data along with human-in-the-loop approach.

6. Access control and intrusion detection: AI systems can be used in access control by replacing or working along with traditional security guards and badge verification systems.

Video surveillance and security systems suffer from certain drawbacks as they operate in public and private areas like airports, highways, etc. The primary concern here is with respect to the manner in which the video may be used or misused. Hence, both governments and private organizations must take relevant steps to lower the impact of video surveillance and security systems on the privacy of people as per specific guidelines.

Summary: The surveillance industry has undergone transformation given the recent enhancements in AI technologies.The training and enhancement of AI models must be done with utmost care to ensure personal privacy as well as human rights.