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Monitoring drivers’ health is crucial for saving lives in emergencies and enabling in-car health applications. The state of the art in pulse monitoring is contact-based sensors which impair the driving experience and have to be applied manually before driving. This paper focuses on automated hyper parameter optimizing the Eulerian Video Magnification (EVM) algorithm, which detects heart rates through non-contact facial camera images, for use in driving scenarios. We conducted a user study where 21 participants performed a driving simulation while their heart rates were recorded by a wearable fitness tracker (serving as ground truth) and facial images with an RGB camera. Our findings indicate that, despite using the optuna library for hyper parameter tuning, the Eulerian Video Magnification algorithm is insufficient for accurate pulse detection in a driving simulator environment.
In order to allow robust obstacle detection for autonomous freight traffic using freight trains or shunting locomotives, several different sensors are required. Humans and other objects must be detected so that the vehicle can stop in time. Laser scanners deliver distance information and are popular in robotics and automation. Cameras deliver further pieces of information on the environment and are especially useful for the classification of objects, but do not deliver distance measurements. Thermal cameras are ideal for the detection of humans based on their body temperature if the surrounding temperature is not too similar. It is only the combination of these different sensors which delivers enough robustness. Therefore a sensor fusion and an extrinsic calibration has to take place. This article presents an approach fusing a 2D and an 8-layer 3D laser scanner with a thermal and a Red-Green-Blue (RGB) camera, using a triangular calibration target taking all six degrees of freedom into account. The calibration was tested and the results validated during reference measurements and autonomous and manually controlled field tests. This sensor fusion approach was used for the obstacle detection of an autonomous shunting locomotive.
In order to increase the robustness of localisation and victim detection in low visibility situations it is necessary to fuse several sensors. The most common sensor used in robotics is the 2D laser scanner which delivers distance measurements. In combination with a camera the gained information can be supported by visual
information about the environment. Thermal cameras are ideal for finding objects with a certain temperature, but they do not deliver distance information. The difficulty in fusing these two sensors is, that a correspondence between each distance measurement and its corresponding pixel within the thermal image needs to be found.
As the laser scanner only displays one plane, this is not an intuitive task. A special triangular calibration target, covering all six degrees of freedom and being visible for both sensors, was developed. In the end the transformation between each laser scan point and its corresponding thermal image pixel is given. This
allows for assigning every laser measurement within the field of view a corresponding thermal pixel. The final application will enable detection of human beings and display the distance required to reach them.