LEADER 02648nam 22004695 450 001 9910838264403321 005 20240322031842.0 010 $a3-8325-9013-7 035 $a(CKB)4100000007650689 035 $a(MiAaPQ)EBC5662536 035 $a5c7aad7c-160c-40fd-a4fc-7583b0dd2d03 035 $a(EXLCZ)994100000007650689 100 $a20190115d2019 ||| | 101 0 $aeng 135 $auruuu---uuuuu 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aPedestrian Detection Algorithms using Shearlets 210 31$aBerlin$cLogos Verlag$d2019 215 $aOnline-Ressource (186 S.) 300 $aPublicationDate: 20190115 311 $a3-8325-4840-8 330 $aLong description: In this thesis, we investigate the applicability of the shearlet transform for the task of pedestrian detection. Due to the usage of in several emerging technologies, such as automated or autonomous vehicles, pedestrian detection has evolved into a key topic of research in the last decade. In this time period, a wealth of different algorithms has been developed. According to the current results on pedestrian detection benchmarks, the algorithms can be divided into two categories. First, application of hand-crafted image features and of a classifier trained on these features. Second, methods using Convolutional Neural Networks in which features are learned during the training phase. It is studied how both of these types of procedures can be further improved by the incorporation of shearlets, a framework for image analysis which has a comprehensive theoretical basis. To this end, we adapt the shearlet framework according to the requirements of the practical application of pedestrian detection algorithms. One main application area of pedestrian detection is located in the automotive domain. In this field, algorithms have to be runable on embedded devices. Therefore, we study the embedded realization of a pedestrian detection algorithm based on the shearlet transform. 606 $aKünstliche Intelligenz$2gnd 606 $aBildverarbeitung$2gnd 606 $aDeep Learning$2gnd 606 $aAutonomes Fahren$2gnd 606 $aObjekterkennung$2gnd 615 07$aKünstliche Intelligenz 615 07$aBildverarbeitung 615 07$aDeep Learning 615 07$aAutonomes Fahren 615 07$aObjekterkennung 676 $a363.12/563 700 $aPfeifer$b Lienhard$4aut$01731842 801 0$bMiAaPQ 801 2$bNZ-WeVUL 906 $aBOOK 912 $a9910838264403321 996 $aPedestrian Detection Algorithms using Shearlets$94144972 997 $aUNINA