3 research outputs found
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Real time occupant detection in high dynamic range environments
The aim of this thesis is to explore strategies for real-time image segmentation of non-rigid objects in a spatio-temporal domain with a stationary camera within an optical high dynamic range environment. Camera, illumination and segmentation techniques are discussed for image processing in environments which are characterized by large intensity fluctuations and hence a high optical dynamic range (HDR), in particular for vehicle interior surveillance.
Since the introduction of the airbag in 1981 numberless lives were saved and bad injuries were avoided. But in recent years the airbag has frequently been in the headlines due to the increasing number of injuries caused by it. To avoid these injuries a new generation of ’smart airbags’ has been designed which shows the ability to inflate in multiple steps and with different volumes. In order to determine the optimal inflation mode for a crash it is necessary to consider information about the interior situation and the occupants of the vehicle. This thesis presents a real-time visual occupant detection and classification system for advanced airbag deployment, utilizing a custom CMOS camera and motion based image segmentation algorithms for embedded systems under adverse illumination conditions.
A novel illumination method is presented which combines a set of images flashed with different radiant intensities, which significantly simplifies image segmentation in HDR environments. With a constant exposure time for the imager a single image can be produced with a compressed dynamic range and a simultaneously reduced offset. This makes it possible to capture a vehicle interior under adverse light conditions without using high dynamic range cameras and without losing image detail. The expansion of this active illumination experiment leads to a novel shadow detection and removal technique that produces a shadow-free scene by simulating an artificial infinite illuminant plane over the held of view. Finally a shadowless image without loss of texture details is obtained without any region extraction phase.
Furthermore, a texture based segmentation approach for stationary cam-eras is presented which is neither effected by sudden illumination changes nor by shadow effects
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Single-imager occupant detection based on surface reconstruction
This thesis introduces a novel framework for a real-time occupant detection system capable of extracting both two- and three-dimensional information using a single imager with active illumination. The primary objective of this thesis is to demonstrate the feasibility of such a low-cost classification system with comparable performance to multi-camera based stereo vision systems. Severe illumination conditions characterised by a frequent and wide illumination fluctuation are also challenging problems addressed in this work. The proposed system is designed to solve a problem of classifying three occupant classes being an adult, a forward-facing child seat, and a rear-facing child seat.
DoubleFlash is employed to eliminate the influence of ambient illumination and to compress the optical dynamic range of target scenes. The idea underlying this technique is to subtract images flashed by different illumination power levels. The extension of this active illumination technique leads to the development of a novel shadow removal technique, called ShadowFlash. By simulating an artificial infinite illuminating plane over the field of view, the technique produces a shadowless scene without losing image details by composing multiple images illuminated from different directions. The ShadowFlash technique is then extended to the temporal domain by employing the sliding n-tuple strategy, which is introduced to avoid the reduction of the original frame rate.
A modified active contour model, facilitated by morphological operations, extracts the boundary of the target object from the shadow-free scenes produced by the ShadowFlash. Based on the brightness information of the image triplet generated by the DoubleFlash, the orientations of the object surface at pixel points are estimated by the photometric stereo method and integrated into the 3D surface by means of global minimisation. The boundary information is used to specify the region of interest to reconstruct. Investigating both the two- and three-dimensional properties of vehicle occupants, 29 features are defined for the training of a neural network. The system is tested on a database of over 84,000 frames collected from a wide range of objects in various illumination conditions. A classification accuracy of 98.9% was achieved within the decision-time limit of three seconds