Mirko Langnickel

Hand-movement-based in-vehicle driver/front-seat passenger discrimination for centre console controls

Successful user discrimination in a vehicle environment may yield a reduction of the number of switches, thus significantly reducing costs while increasing user convenience. The personalization of individual controls permits conditional passenger enable/driver disable and vice versa options which may yield safety improvement. The authors propose a prototypic optical sensing system based on hand movement segmentation in near-infrared image sequences implemented in an Audi A6 Avant. Analyzing the number of movements in special regions, the system recognizes the direction of the forearm and hand motion and decides whether driver or front-seat passenger touch a control. The experimental evaluation is performed independently for uniformly and non-uniformly illuminated video data as well as for the complete video data set which includes both subsets. The general test results in error rates of up to 14.41% FPR / 16.82% FNR and 17.61% FPR / 14.77% FNR for driver and passenger respectively. Finally, the authors discuss the causes of the most frequently occurring errors as well as the prospects and limitations of optical sensing for user discrimination in passenger compartments.

Car-seat occupancy detection using a monocular 360° NIR camera and advanced template matching

The integration of seat occupancy detection systems is one of the most recent developments in automobile production. These systems prevent the deployment of airbags at unoccupied seats, thus avoiding the considerable cost imposed by the replacement of airbags. Seat-occupancy detection system can also be used to improve passenger comfort, e.g. by an occupation-dependent control of air-conditioning systems. This paper describes an inexpensive and versatile optical seat-occupancy detection system. Different approaches to pattern matching and the impact of local normalization, edge detection, multi-algorithm and temporal matching-score fusion are evaluated for each individual seat using a test set of 53,928 frames further classified in uniform and non-uniform illumination conditions. The results of these tests yield Equal Error Rates for uniform/non-uniform illumination of as low as 3.05%/1.68% for the front left seat, 2.17%/0.69% for the front right seat, 5.86%/4.01% for the rear left seat, 10.99%/11.07% for the rear center seat and 5.63%/1.84% for the rear right seat. The test results indicate that at least the two seat rows should be treated differently in terms of the selection of classification algorithms.

The feasibility test of state-of-the-art face detection algorithms for vehicle occupant detection

Vehicle seat occupancy detection systems are designed to prevent the deployment of airbags at unoccupied seats, thus avoiding the considerable cost imposed by the replacement of airbags. Occupancy detection can also improve passenger comfort, e.g. by activating air-conditioning systems. The most promising development perspectives are seen in optical sensing systems which have become cheaper and smaller in recent years. The most plausible way to check the seat occupancy by occupants is the detection of presence and location of heads, or more precisely, faces. This paper compares the detection performances of the three most commonly used and widely available face detection algorithms: Viola- Jones, Kienzle et al. and Nilsson et al. The main objective of this work is to identify whether one of these systems is suitable for use in a vehicle environment with variable and mostly non-uniform illumination conditions, and whether any one face detection system can be sufficient for seat occupancy detection. The evaluation of detection performance is based on a large database comprising 53,928 video frames containing proprietary data collected from 39 persons of both sexes and different ages and body height as well as different objects such as bags and rearward/forward facing child restraint systems.