Intrusion detection in rural areas using A.I. drones
In agricultural security, A.I. technology has made it easier to deliver proactive and more efficient land surveillance services. This blog will explain an interesting use case.
Recently, I was reading about a new case study describing innovative solutions to solve the challenge to real-time monitor wildlife in a national wildlife reserve. Their approach caught me and inspired me to understand how it can be used for other video and security surveillance too.
Challenges with intrusion detection for land surveillance
Farmers, Nature Conservation and owners of large land areas are committed to monitoring any unwanted human activity on their property. This means they must provide surveillance to maintain a safe and secure area. Identifying intrusions across large areas is a challenge.
Areal surveillance solution with new A.I. technologies
In the case study, the researchers discussed how video surveillance for drones supports their goal to protect wildlife by detecting nearby intrusion of human activity with no to very little cellphone coverage. Today, human activity can be identified immediately by using the power of A.I. on edge, leveraging CNN and GAN algorithms, and focusing on behaviour detection.
Where computational tasks are performed directly on drones.
In the past this was impossible because drones needed to be connected to the internet at all times, sending large quantities of data. Edge computing has resolved this issue.
Business Value of A.I. equipped drones
The A.I.-equipped drones provide business value to land owners today. The data can be relayed, in real-time, including high-resolution images and videos, when suspicious behaviour patterns are detected. Therefore reducing response times and facilitating preventive measures to ensure perimeter security where needed.
Future of A.I for security monitoring
Looking ahead, the future of A.I.-driven security holds great potential. Technology solution providers can gain deeper insights into monitoring security activities by refining and expanding the data to train behaviour detection models. Moreover, integrating advanced analytics and predictive algorithms will enable proactive security measures, ensuring a safer and more secure environment.
A.I.-equipped drones in urban areas
In urban areas, drone surveillance can be successful too. The use case has demonstrated the power of the technology they have used. These drones can also support urban surveillance by using A.I. on edge.
Conclusion growth towards autonomous surveillance
As A.I. technology continues to advance and collaborations flourish, autonomous surveillance driven by A.I. will play a pivotal role in maintaining the safety and security of public open areas. Where the added value of autonomous surveillance should always stick to the principles of HITL.
If you still have questions after reading this blog, don't hesitate to get in touch.