Safer roads. Safer herds.
CamelTrack detects camels with computer vision and IoT, helping herders monitor their animals and warning drivers when camels approach roads.
The problem
Camel–vehicle collisions are deadly for people and animals.
In arid regions, camels crossing roads cause serious accidents. Their size, strength and unpredictable movements make collisions especially severe, while warning signs and fences lose effectiveness over time.
machine learning and computer vision detect and recognise camels.
connected devices enable remote monitoring and alerts.
alerts drivers to potential animal crossings.
Source: system design from the founding research paper (DeSE, IEEE, 2023).
How it works
How it works
A clear path from data to decision.
Watch
Cameras monitor grazing areas and roadsides.
Detect
Computer vision recognises camels in the scene.
Track
IoT devices report locations to herders.
Warn
Drivers receive alerts about nearby animals.
Product
What makes it different.
Every feature is grounded in the founding research.
Camel detection
Machine-learning models trained to recognise camels.
IoT monitoring
Remote tracking of herd locations.
Driver warnings
Alerts near roads to prevent collisions.
Better than fences
Adaptive technology where static measures lose effectiveness.
Who it's for
Who it's for.
Livestock owners
Monitor and track herds remotely.
Road authorities
Reduce animal–vehicle accidents.
Wildlife & rural agencies
Protect animals and habitats.
The research
Built on peer-reviewed research.
- Animal–vehicle accidents involving camels have increased in recent decades.
- Warning signs and fences have drawbacks and lose effectiveness over time.
- Proposes machine learning and computer vision for camel detection and recognition.
- Supports warnings to drivers to mitigate camel–vehicle accidents.
Madi, M., et al. (2023). Camel Detection and Monitoring Using Image Processing and IoT. International Conference on Developments in eSystems Engineering (DeSE), 305–308. IEEE.
Team
The team behind it.
Founded in 2023 by a student team from the faculty below.
Faculty of Science (Computer Science)
Computer vision, IoT and software development.
FAQ
Questions we hear often.
Does it work at night?
Night operation depends on camera choice; infrared cameras are part of our roadmap.
Can it track individual camels?
IoT devices support location tracking for herd monitoring.
Is this a finished commercial product?
It is a research-based student startup moving from validated prototype toward pilots with real users.
Can we partner or run a pilot?
Yes. We are looking for organisations willing to test the solution in real conditions and share feedback.
Bring CamelTrack to your organisation.
We are onboarding pilot partners now. Read the research and get in touch.