Postgraduate Diploma in Non-Conventional Cameras: Event/Thermal/Multispectral
Online 800-hour Postgraduate Diploma in Non-Conventional Cameras: Event/Thermal/Multispectral. 8 modules covering ISO 21448:2022 (SOTIF), ISO 26262:2018 and OpenCV. Assigned tutor and open enrolment.
Take the controls.
Run a real engineering trade-off from this programme. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Compare the limitations of the RGB camera with emerging sensors.
Process event camera data using appropriate representations.
Develop event-based perception algorithms.
Apply thermography to night-time detection.
Use multispectral imaging to analyse road surface condition.
Calibrate and synchronise sensors of different modalities.
Assess the integration and safety of non-conventional cameras.
Who it's for
- Computer vision engineer who wants to specialise in Non-Conventional Cameras: Event/Thermal/Multispectral.
- Optical sensor engineer who wants to specialise in Non-Conventional Cameras: Event/Thermal/Multispectral.
- Perception engineer who wants to specialise in Non-Conventional Cameras: Event/Thermal/Multispectral.
- Neuromorphic vision researcher who wants to specialise in Non-Conventional Cameras: Event/Thermal/Multispectral.
- ADAS integration engineer who wants to specialise in Non-Conventional Cameras: Event/Thermal/Multispectral.
- Thermography specialist who wants to specialise in Non-Conventional Cameras: Event/Thermal/Multispectral.
- Graduates in engineering or science seeking to enter the automotive sector.
What you'll work with
- ISO 21448:2022 (SOTIF)
- ISO 26262:2018
- EMVA 1288
- Reglamento ONU n.º 152
- IEEE 2020-2024
- ISO/SAE 21434:2021
- ISO 16505:2019
8 modules · 800 h
1.1Limitations of the conventional CMOS sensor1.2Dynamic range and rolling shutter1.3Useful electromagnetic spectrum in automotive1.4Overview of emerging sensors1.5HDR cameras and LED flicker1.6Time-of-flight cameras1.7Polarimetric cameras1.8Cost and industrial maturity1.9Structured light cameras in the cabin1.10Case study: sensor selection matrix
2.1Principle of the dynamic vision sensor2.2Representation of asynchronous events2.3Advantages in latency and dynamic range2.4Event noise and artefacts2.5Voxels and time surfaces2.6Intensity reconstruction with E2VID2.7Optical flow with events2.8DSEC and MVSEC datasets2.9Hybrid event-and-frame sensors2.10Lab: event processing with Python
3.1Object detection with events3.2Spiking neural networks3.3Neuromorphic hardware3.4Fusion of events and frames3.5Visual odometry with events3.6Detection of flashes and traffic lights3.7Stereo depth with events3.8Evaluation and benchmarks3.9High-speed tracking with events3.10Practical: pedestrian detector with events
4.1Infrared radiation and Planck's law4.2Uncooled LWIR microbolometers4.3Radiometric calibration and NUC4.4Pedestrians and animals at night4.5Performance in fog and glare4.6FLIR ADAS thermal dataset4.7RGB-thermal fusion4.8Germanium windows and integration4.9Thermal resolution and NETD4.10Lab: night-time detection with thermal imaging
5.1Visible, NIR and SWIR bands5.2Mosaic filter sensors5.3InGaAs SWIR cameras5.4Ice detection and road surface condition5.5Material classification5.6Radiometric correction and illumination5.7Spectral dimensionality reduction5.8Applications in agricultural vehicles5.9Spectral indices and their calculation5.10Practical: classification of wet surfaces
6.1Geometric calibration of thermal cameras6.2Heated calibration patterns6.3Calibration of event cameras6.4Registration across modalities6.5Hardware time synchronisation6.6Distortion correction6.7Calibration validation6.8Field maintenance6.9Parallax and joint mechanical mounting6.10Lab: joint RGB-thermal calibration
7.1Embedded processing architecture7.2Bandwidth and GMSL interfaces7.3Euro NCAP requirements for night-time AEB7.4SOTIF analysis of emerging sensors7.5Tests in darkness and backlight7.6Cybersecurity of the camera link7.7Cost and supply chain7.8Sensor business case7.9Image quality according to IEEE 20207.10Case study: thermal camera integration plan
8.1Definition of the perception problem8.2Choice of sensing modality8.3Data acquisition or selection8.4Calibration and registration8.5Algorithm development8.6Evaluation against an RGB camera8.7Vehicle integration analysis8.8Cost and risk assessment8.9Report and presentation8.10Final review with the tutor
How you'll learn
100 % online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. No fixed timetable: students progress at their own pace within the intake, which makes it possible to combine the programme with work.
Each module combines theory, guided reading of the original standard and applied exercises. The software used is OpenCV, PyTorch, Metavision SDK, MATLAB, ROS 2, in free, academic or evaluation versions. Tests are studied using real procedures for night-time pedestrian detection with a thermal camera, optical flow estimation with an event camera, and classification of road surface condition with SWIR imaging. Assessment is continuous, module by module, plus the final integrating project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Compare the limitations of the RGB camera with emerging sensors.
Process event camera data using appropriate representations.
Develop event-based perception algorithms.
Apply thermography to night-time detection.
Use multispectral imaging to analyse road surface condition.
Where this takes you
Requirements
- Prior technical training or professional experience in the sector. No university degree is required.
- Internet connection and a computer capable of running the programme's software.
- Sufficient level of Spanish to follow technical standards documentation.
Ready to start?
Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.
Frequently asked questions
The programme comprises 800 hours in total, spread over 6 to 8 months. That is an average of 10 to 15 hours per week, compatible with full-time work.
You will work with OpenCV, PyTorch, Metavision SDK, MATLAB, ROS 2. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.
A SEIUM own diploma upon passing the continuous assessment and the integrating project. It is in-house training, not an official Ministry title.
Yes. Modules passed in a course or diploma in the same area are credited towards the advanced programme. Check the credit transfer policy when you enrol.
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