/Sensor fusion for object detection and scene analysis

Sensor fusion for object detection and scene analysis

PhD - Gent | More than two weeks ago

Develop novel sensor fusion paradigms that far exceed the performance of traditional architectures in border cases that do not conform to standard models, with far reaching benefits in fields of autonomous driving, traffic monitoring, industrial automation, security & surveillance.

Many smart mobility and industrial automation applications rely on object detection and tracking. Examples include autonomous vehicles, smart traffic infrastructure, mobile robots, security & surveillance. Sensor fusion is essential for safety critical systems, both for robustness (redundant information) and accuracy (complementary information). Standard sensor fusion techniques are unable to cope with difficult circumstances, e.g. partial blindness of a sensor due to adverse weather or light conditions, or objects of interest partly occluded by static scene elements. In this research, we will improve upon the state of the art for fusion of cameras (RGB cameras, thermal cameras, DVS) with radar and/or lidar, by accurately modeling their behavior under different complicating circumstances, and by building a probabilistic framework to assess the reliability of the fused output. Additionally, novel forms of sensor fusion will be explored that include feedback loops and low-level communication between the sensors. These novel concepts require detailed theoretical modelling of the data dependencies to ensure both maximum performance and graceful degradation in difficult conditions.

This topic builds on extensive prior experience within IPI regarding sensor fusion for autonomous driving, traffic monitoring and industrial safety.

Required background: Engineering or Mathematics, with strong interest in Computer Science

Type of work: 45% theoretical modeling, 45% algorithm implementation, 10% field testing

Supervisor: Hiep Luong

Co-supervisor: Wilfried Philips

Daily advisor: David Van Hamme

The reference code for this position is 2022-111. Mention this reference code on your application form.