In this paper a novel spline-based multi-lane detection and tracking system is proposed. Reliable lane detection and tracking is an important component of lane departure warning systems, lane keeping support systems or lane change assistance systems. The major novelty of the proposed approach is the usage of the so-called Catmull-Rom spline in combination with the extended Kalman filter tracking. The new spline-based model enables an accurate and flexible modeling of the lane markings. At the same time the application of the extended Kalman filter contributes significantly to the system robustness and stability. There is no assumption about the parallelism or the shapes of the lane markings in our method. The number of lane markings is also not restrained, instead each lane marking is separately modeled and tracked. The system runs on a standard PC in real time (i.e. 30 fps) with WVGA image resolution (752 × 480).The test vehicle has been driven on the roads with challenging scenarios, like worn out lane markings, construction sites, narrow corners, exits and entries of the highways, etc., and good performance has been demonstrated. The quantitative evaluation has been performed using manually annotated video sequences.

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This paper presents a thorough introduction to the real time video surveillance system which has been developed at Bosch Corporate Research considering robustness as the major design goal. A robust surveillance system should especially aim for a low number offalse positives since surveillance guards might get distracted by too many alarms caused by, e.g., moving trees, rain, small camera motion, or varying illumination conditions. Since a missed security related event could cause a serious threat for an installation site, the before mentioned criterion is obviously not suf cient for designing a robust system and thus a low number offalse negatives should simultaneously be achieved. Due to the fact that the false negative rate should ideally be equal to zero, the surveillance system should be able to cope with varying illumination conditions, low contrast and occlusion situations. Besides presenting the building blocks of our video surveillance system, the measures taken to achieve robustness will be illustrated in this paper Since our system is based on algorithms for video motion detection, which has been described e.g. in [1], the previous set of algorithms had to be extended to feature a complete video content analysis system. This transition from simple motion detection to video content analysis is also discussed in the following. In order to measure the performance of our system, quality measures calculated for various PETS sequences will be presented.

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