MLT and OEM Automatic case study
Machine Learning Technologies (MLT) is a pioneering company focused on behavioural analysis through advanced sensing and machine vision. MLT set out to develop a behavioural analysis system capable of operating in dynamic, high-traffic environments such as airports. The company blends rigorous research with real-world applications, aiming to revolutionise how environments like airports understand and respond to human behaviour.
MLT harnesses advanced artificial intelligence and deep-learning computer vision techniques to solve real-world automation challenges. By integrating AI solutions directly into the control and field layers, MLT enhances security, process control, predictive maintenance, and real-time decision-making for its clients.
Enter OEM
Working together, MLT and OEM recognised the need for a robust, scalable solution capable of handling complex data processing at the edge without relying on cloud infrastructure. MLT approached OEM Automatic for hardware expertise, where they were introduced to Tom Bunn, one of OEM’s machine vision specialist. Tom played a pivotal role in guiding MLT through edge computing technology and system design.
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Tom has approximately 10 years of industry experience, including five years with OEM. When asked about MLT and his relationship with them, he stated:
“This project was really interesting, as it’s something that a lot of companies have spoken about, but MLT have made it work. MLT’s knowledge of AI and how to apply it is top class, so it was easy for us to support them by recommending the best hardware and providing test units, then scaling alongside them as demand grows.”
The systems that MLT provide:
PaxShield – Human Behaviour Analysis
Provides real-time human behavioural analysis across departure halls and baggage handling environments. The system identifies unsafe or abnormal behaviours during day-to-day operations, including encroachment into restricted zones, unsafe passenger actions near moving equipment, and behavioural patterns that increase the risk of incidents. PaxShield operates passively alongside existing infrastructure, enhancing safety awareness without disrupting passenger flow.
BagShield – Baggage Analytics
Delivers advanced baggage analytics across live conveyor systems. Using AI-based vision and rules-driven logic, the system detects and classifies abnormal baggage conditions, including trapped bags, loose straps or handles, unstable orientations, and oversized or unusually shaped items unsuitable for standard baggage lines. BagShield enables early intervention to prevent jams, damage, and downstream system failures.
BagTrakX – Optical & Sticker Recognition
Provides optical and label-based recognition to support baggage traceability and flow assurance. The system enables bags to be identified and tracked through complex handling environments, supporting safe and efficient movement from origin to destination, while assisting with investigation, recovery, and operational optimisation.
FlowShield – Escalator & Travelator Safety Analytics
Applies human behaviour and baggage analytics to escalators and travelators. The system detects unsafe passenger behaviour, unstable baggage, and conditions that could lead to entrapment or injury, enabling preventative intervention before incidents occur. FlowShield enhances safety in high-risk passenger movement areas without requiring physical modification to existing equipment.
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