Analysis and assessment of far infrared sensor performance parameters and their impact on pedestrian detection

In this contribution four different far infrared sensor setups with different optical configurations are evaluated based on their performance for pedestrian detection. The focus of the measurements is on the impact of resolution and sensitivity on the detection performance. To overcome the expensive and time consuming process of ground truth generation for multiple sensors, an approach for reusing available high sensitivity reference data is presented. Each of the sensors is evaluated at its basic and two simulated resolutions. Classifiers are trained on specially transformed reference data with characteristics of sensors with degraded performance parameters. For the evaluation of the classifiers, data of real world road scenarios is collected with the target sensors mounted in parallel in a test vehicle, following a detailed script for recording a pedestrian scene test catalogue. This allows for a direct analysis and comparison of the different sensors and their impact on the detection performance.