Thesis Details
Fine-Grained Recognition and Re-Identification of Vehicles Using Advanced Feature Extraction
The aim of this theses was to analyze and improve methods used for fine-grained vehicle recognition and vehicle re-identification. The proposed method can be used both for recognition and re-identification. It was based on 3D bounding boxes, which were used to detect the vehicle on the image and then the vehicle was normalized by unpacking into 2D. Improvement of this method was done by determining direction of the vehicle and distinguishing between front and rear while unpacking the vehicle. This proposed method improved the existing method based on 3D bounding boxes for recognition, reducing error up to 13 % in single sample accuracy and up to 17 % track accuracy. However, no improvement was gained for vehicle re-identification using LFTD aggregation.
convolutional neural network, vehicle re-identification, Fine-grained vehicle recognition,3D bounding box
Bidlo Michal, doc. Ing., Ph.D. (DCSY FIT BUT), člen
Čadík Martin, doc. Ing., Ph.D. (DCGM FIT BUT), člen
Křivka Zbyněk, Ing., Ph.D. (DIFS FIT BUT), člen
Rogalewicz Adam, doc. Mgr., Ph.D. (DITS FIT BUT), člen
@bachelorsthesis{FITBT22078, author = "Ond\v{r}ej Dosed\v{e}l", type = "Bachelor's thesis", title = "Fine-Grained Recognition and Re-Identification of Vehicles Using Advanced Feature Extraction", school = "Brno University of Technology, Faculty of Information Technology", year = 2019, location = "Brno, CZ", language = "english", url = "https://www.fit.vut.cz/study/thesis/22078/" }