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Optical Baggage Tracking & Identification

Artificial Intelligence and Computer Vision helping to keep track of the baggage

From challenges to solutions

Keeping track of ever growing numbers of baggage across large infrastructure facilities can be costly and error prone.


Enhance and later replace bar code based systems by marker-less computer vision approaches.


Catalysts adapted state of the art machine learning approaches from other identification problem domains (e.g. face recognition) to the task of baggage identification.


Tracking baggage using conventional means (like bar codes etc.) is both costly and error prone, for example, bar codes can be lost. Furthermore, a continuous tracking of baggage within large scale installations requires a large number of tracking sensors – which are both costly in acquisition and maintenance.


By augmenting existing tracking solution and camera based systems the overall tracking accuracy is improved and new use cases can be covered.

Camera systems mounted at key locations of the baggage transport logistic process (e.g. at check in counters, before and after security screenings, at aircraft loading bays, …) can both detect and track baggage and thus ensure a complete audit trail.

This can be used in a variety of use cases:

  • Security: Was the baggage being loaded into the aircraft handled at the security screening checkpoint?
  • Lost and found: Did the checked in baggage make it into the aircraft?
  • Liability: Where was a baggage lost and by whom? At the airport? By the airline?
  • Theft prevention: Did a baggage item unloaded from a plane arrive at the baggage reclaim area? If so, how long was it present there?


Instead of developing a large centralized system Catalysts divided the solution into two main parts:

A small and cost effective camera and image processing service
and a centralized identification and tracking service.

Using this architecture images are processed in near real time. This means that the video doesn’t need to be streamed over the network, what contributes to low network load. Also, central processing server is not needed (no single point of failure).

The image processing service:

Catalysts created a multi stage real time image processing pipeline consisting of the following steps:

1. Object detection: detection, localization and classification of baggage on the video stream.

  • What objects are visible?
  • Where are these objects located on the video?
  • What type of objects are visible? Persons? Baggage? Cargo container?

2. Instance segmentation: which parts of the image belong to which object exactly?

  • Multiple, potentially touching and overlapping baggage?
  • Persons handling and occluding baggage items?

3. Object Fingerprinting: finding a numerical and representation of the characteristic of the detected baggage.

  • What features distinguish the baggage most from others?
  • There are millions of black trolleys, but maybe only one has a red Catalysts logo on it?
The identification and tracking service:

A server receives object fingerprints from the image processing services and stores them in a database. By comparing these fingerprints – a quick and easy operation – the server can easily compare current detection of baggage items to previous detection and thus build a complete audit trail.

  • Do you need to track individual items in your production plant?
  • Would your business benefit from real time tracking of assets?
    • Parking lot monitoring
    • Cargo container management
  • Automated alerts when specific objects leave or enter virtual geo fences?

Ihr Ansprechpartner

Alessio Montuoro

Emerging Technologies, Computer Vision


Profile on Xing, LinkedIn, Skype

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