Absrrucr-In this paper we present a recognition scheme which is both reIiahle and fast, The scheme CtJILlpriSeS the simultaneous harmonized use of three powerful detection algorithm, the hyper permutation rietwork (HPN), a hirrarchical contour matching (HCM) algorithm and a cascaded classifier approach. Each algorithm is evaluated separately and afterwards, based on the evaluation results, the fusion of the detection results is performed by a partick filter approach. T. INTRODUCTION Driver assistance systems supporting the driver at night are of increasing interest in the car market. The first generation or non-warning night vision systems has been already introduced. Current research focuses on the second generation of night vision sysleius integrating a warning function. An indispensable feature of the second generation systems is the capability of reliably detecting vulnerable I-oad users (VRUs), like pedestrians up to distances of 100m (330ft). Human and animal thermal radiation has its peak within the far-infrared (FIR. A = 6-I5pm) wave band. thus ohstacles can act as radiation emitters and no il!umination is necessary. In this contribution we used a microbolonieter technology sensor, sensitive between 7 and 14 micrometers with spatial, temporal and spectral resolution of 164xI29px' ' 2 25Hz, l4hit. Automotive pedestrian detection from FIR data is it challenge in many ways (Fig. 1) . The segmentation process has to cope with moving objects in front of an itself moving background, eliminating any background suhtraction strategies. Additionally, no analyrical representation of pedestrian shape and/or texture can be found. as i.e. in case of traffic sign deiection. where the objects to be segmented are of circular or triangular shape and are provided with a This research was supported by NIRWARN, RMBF lJlM31573. well known color distribution. Pedestrian movement causes shape inconsistency over time, too. Texture inconsistency comes from different clothing and is -especially in winter time -strongly dependent on how long the human already stayed outdoor. A.Broggi and T.Graf et. al. solved the problem of pedestrian recognition in FIR images using multiresolution texture symmetry, edge. symmetry and edge density ROl extraction, together with a texture and shape correlation validation step, based on shaded 3-D pedestrian models [I]. Liu and Fujimura's strategy applies intensity thresholding, followed by a motion constraint computed from stereo data and aspect ratiolsize discrimination 121. In 131. E X u and KFujimura used intensity thresholds followed by a comhination of support-vector-machine classification C. P a p r .striicfiire After the first introductory section, including related work and motivation, section two shortly introduces currently promising pedestrian segmentation methods, partially originating from visible wavelength image processing. Section three deals with our way to evaluate detector performance and presents the evaluation results for each method of its own, pointing out its strengths and weaknesses. Based on these results, in section four we introduce a novel detection system for FIR-data, with object list level output, performing the detection task in realtime on a 3 GHz Pentium 1V. We close with an overview on problems still unsolved and suggest further development steps. From examinations we know that schemes relying on intensity, uniformity or symmetry assumptions fail to reliably detect VRUs (compare fig1 left and right). This is why our approach completely operates on pattern correlation and trained pattern classification. For the task of pedestrian -325 -0-7803-8961
In REF the authors use far infrared cameras, hyper permutation networks, hierarchical contour matching and a cascaded classifier approach.
A multiple detector approach to low-resolution FIR pedestrian recognition
IEEE Proceedings. Intelligent Vehicles Symposium, 2005.
IEEE Proceedings. Intelligent Vehicles Symposium, 2005.
Computer Science