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Master'sAutonomous, ADAS & Software-Defined Vehicle Applications open

Master’s in Event, Thermal and Multispectral Camera Engineering

Online master's programme of 1,900 hours in event, thermal and multispectral camera engineering, structured in 12 modules covering unconventional image sensors, event-data processing, infrared thermography, multispectral imaging, multimodal fusion, automotive integration, validation and regulation, with an assigned technical tutor.

Duration1900 h
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Compare unconventional imaging modalities for automotive use.

02

Process data from event cameras.

03

Apply infrared thermography to road-user detection.

04

Analyse multispectral images to classify surfaces.

05

Design multimodal fusion systems.

06

Validate sensors in night-time and adverse conditions.

07

Develop a perception system based on advanced sensing.

Profile

Who it's for

  • Computer vision engineers moving into event, thermal and multispectral imaging.
  • Optical sensor engineers working on new imaging modalities.
  • Researchers in neuromorphic vision.
  • Night-time perception engineers.
  • Multimodal fusion engineers.
  • Sensor validation engineers.
  • Engineering or science graduates looking to enter the automotive sector.
Standards & tools

What you'll work with

  • ISO 26262 (functional safety of road vehicles)
  • ISO 21448 (safety of the intended functionality, SOTIF)
  • UN ECE R152 (advanced emergency braking systems)
  • IEC 60825-1 (safety of laser products)
  • ISO 16505 (camera monitor systems)
  • Regulation (EU) 2021/821 (export control of dual-use items)
  • ISO 18434-1 (condition monitoring with thermography)
  • UN ECE R10 (vehicle electromagnetic compatibility)
  • Prophesee Metavision SDK
  • PyTorch
  • OpenCV
  • ROS 2
  • MATLAB
Syllabus

12 modules · 1900 h

  1. 1.1Limitations of conventional cameras
  2. 1.2Event cameras
  3. 1.3Infrared thermal cameras
  4. 1.4Multispectral and hyperspectral cameras
  5. 1.5Short-wave infrared (SWIR) cameras
  6. 1.6Time-of-flight cameras
  7. 1.7Polarimetric cameras and reflections
  8. 1.8Comparison of modalities
  9. 1.9Automotive applications
  10. 1.10Case study: sensor selection for night-time detection
  1. 2.1Principle of DVS sensors
  2. 2.2Event representation
  3. 2.3Latency and dynamic range
  4. 2.4Noise and contrast threshold
  5. 2.5DAVIS and Prophesee sensors
  6. 2.6Event data formats
  7. 2.7Accumulation into frames
  8. 2.8Time surfaces
  9. 2.9Calibration of event cameras
  10. 2.10Lab: event visualisation with Metavision
  1. 3.1Optical flow from events
  2. 3.2Intensity reconstruction
  3. 3.3Ego-motion estimation
  4. 3.4Object detection with events
  5. 3.5Spiking neural networks
  6. 3.6Convolutional networks with events
  7. 3.7Neuromorphic event processing
  8. 3.8Loihi and similar neuromorphic hardware
  9. 3.9DSEC and MVSEC datasets
  10. 3.10Practical: pedestrian detection with events
  1. 4.1Thermal radiation and emissivity
  2. 4.2LWIR and MWIR bands
  3. 4.3Uncooled microbolometers
  4. 4.4NETD and thermal resolution
  5. 4.5Germanium and chalcogenide optics
  6. 4.6Radiometric calibration of the sensor
  7. 4.7Non-uniformity correction
  8. 4.8Performance in fog and smoke
  9. 4.9Cost and integration
  10. 4.10Workshop: thermal image analysis
  1. 5.1Pedestrian detection in thermal
  2. 5.2FLIR ADAS and KAIST datasets
  3. 5.3Transfer learning from visible
  4. 5.4Fusion of visible and thermal imagery
  5. 5.5Multimodal image alignment
  6. 5.6Animal detection
  7. 5.7Night-time cyclist detection
  8. 5.8Metrics by condition
  9. 5.9Real-time deployment
  10. 5.10Lab: thermal detector with YOLO
  1. 6.1Spectral bands and filters
  2. 6.2Sensors with spectral mosaic
  3. 6.3RGB cameras with near infrared
  4. 6.4Spectral signature of materials
  5. 6.5Classification of road surfaces
  6. 6.6Ice and water detection
  7. 6.7SWIR cameras and fog penetration
  8. 6.8Processing of hyperspectral cubes
  9. 6.9Dimensionality reduction
  10. 6.10Practical: road-surface classification with multispectral imaging
  1. 7.1Cross-modality calibration
  2. 7.2Geometric image registration
  3. 7.3Pixel-level fusion
  4. 7.4Feature-level fusion
  5. 7.5Decision-level fusion
  6. 7.6Attention-based fusion networks
  7. 7.7Handling missing modalities
  8. 7.8Fusion with radar and LiDAR
  9. 7.9Evaluation of fusion gain
  10. 7.10Workshop: RGB-thermal fusion with transformers
  1. 8.1Night vision for the driver
  2. 8.2Night-time detection of vulnerable road users
  3. 8.3Cabin monitoring
  4. 8.4Road-condition detection
  5. 8.5Perception in smoke and dust
  6. 8.6Applications in machinery and mining
  7. 8.7Animal warning systems
  8. 8.8Safety in tunnels
  9. 8.9Integration into ADAS
  10. 8.10Case study: night-time AEB with thermal imaging
  1. 9.1Camera interfaces and protocols
  2. 9.2Real-time embedded processing
  3. 9.3Thermal management of sensors
  4. 9.4Integration in the front end and windscreen
  5. 9.5Automotive qualification
  6. 9.6Synchronisation with other sensors
  7. 9.7Bandwidth and compression
  8. 9.8Camera cybersecurity
  9. 9.9Production costs
  10. 9.10Lab: camera integration in ROS 2
  1. 10.1Thermal calibration targets
  2. 10.2Reference blackbodies
  3. 10.3Night-time track testing
  4. 10.4Testing with thermal dummies
  5. 10.5Testing in fog and smoke
  6. 10.6Performance metrics
  7. 10.7Simulation of unconventional sensors
  8. 10.8Reproducibility of tests
  9. 10.9Validation documentation
  10. 10.10Practical: test plan with a thermal dummy
  1. 11.1Euro NCAP night-time requirements
  2. 11.2Pedestrian AEB under R152
  3. 11.3Functional safety of cameras
  4. 11.4SOTIF in new modalities
  5. 11.5Export control of thermal cameras
  6. 11.6Electromagnetic compatibility standards
  7. 11.7Privacy in cabin cameras
  8. 11.8Eye safety in active cameras
  9. 11.9Component certification
  10. 11.10Workshop: regulatory analysis of a thermal camera
  1. 12.1Definition of the use case
  2. 12.2Selection of modalities
  3. 12.3Design of the acquisition system
  4. 12.4Development of detection algorithms
  5. 12.5Design of the multimodal fusion
  6. 12.6Experimental validation in the vehicle
  7. 12.7Cost and integration analysis
  8. 12.8Conclusions and future work
  9. 12.9Master's Thesis report
  10. 12.10Master's Thesis: defence before a panel
Methodology, software & labs

How you'll learn

The programme is 100% online on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within the intake, so you can combine it with your job.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is Prophesee Metavision SDK, PyTorch, OpenCV, ROS 2 and MATLAB, in free, academic or evaluation versions. Testing is studied through real procedures for night-time tests with thermal dummies, pedestrian detection with event cameras and multispectral surface classification. Assessment is continuous by module, plus the final Master's Thesis.

Standards covered: ISO 26262, ISO 21448, UN ECE R152, IEC 60825-1, ISO 16505, Regulation (EU) 2021/821 (dual use), ISO 18434-1, UN ECE R10.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of ISO 26262, ISO 21448 and UN ECE R152 applied to real cases, not as a theoretical annex.
Industry software:Prophesee Metavision SDK, PyTorch, OpenCV, ROS 2 and MATLAB, in free, academic or evaluation versions.
Master's Thesis with defence:An advanced-sensing perception system, from use-case definition to experimental validation in the vehicle, defended before a panel.
Flexible format:100% online, no fixed timetables, compatible with full-time work.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Use case and modalities
Define the night-time or adverse-condition use case and select the imaging modalities.
Acquisition system design
Design the acquisition chain, calibration and integration in ROS 2.
Detection algorithms
Develop detectors for event and thermal data, such as a YOLO-based thermal detector.
Multimodal fusion
Design and evaluate RGB-thermal fusion and measure the fusion gain.
Master's Thesis
Validate the system experimentally in the vehicle and analyse cost and integration, defended before a panel.
Career opportunities

Where this takes you

Computer Vision EngineerOptical Sensor EngineerNeuromorphic Vision ResearcherNight-time Perception EngineerMultimodal Fusion EngineerSensor Validation Engineer
Admissions & scholarships

Requirements

  • University degree in engineering, science or architecture, or equivalent demonstrable professional experience.
  • Internet connection and a computer capable of running the programme's software.
  • English sufficient to read technical standards.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

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FAQ

Frequently asked questions

The programme totals 1,900 hours over 12–14 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.

You work with Prophesee Metavision SDK, PyTorch, OpenCV, ROS 2 and MATLAB, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.

A SEIUM Institute diploma, awarded on passing the continuous assessment and the Master's Thesis. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.

Yes. Modules passed in a course or postgraduate diploma in the same area are recognised in the higher programme. Ask about the recognition policy when you enrol.

A university degree in engineering, science or architecture (or equivalent professional experience), a computer able to run the software, and English sufficient to read technical standards.

Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.

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