In this paper, we focus on fine-grained recognition of vehicles mainly in traffic surveillance applications. We propose an approach that is orthogonal to recent advancements in fine-grained recognition (automatic part discovery and bilinear pooling). In addition, in contrast to other methods focused on fine-grained recognition of vehicles, we do not limit ourselves to a frontal/rear viewpoint, but allow the vehicles to be seen from any viewpoint. Our approach is based on 3-D bounding boxes built around the vehicles. The bounding box can be automatically constructed from traffic surveillance data. For scenarios where it is not possible to use precise construction, we propose a method for an estimation of the 3-D bounding box. The 3-D bounding box is used to normalize the image viewpoint by “unpacking” the image into a plane. We also propose to randomly alter the color of the image and add a rectangle with random noise to a random position in the image during the training of convolutional neural networks (CNNs). We have collected a large fine-grained vehicle data set BoxCars116k, with 116k images of vehicles from various viewpoints taken by numerous surveillance cameras. We performed a number of experiments, which show that our proposed method significantly improves CNN classification accuracy (the accuracy is increased by up to 12% points and the error is reduced by up to 50% compared with CNNs without the proposed modifications). We also show that our method outperforms the state-of-the-art methods for fine-grained recognition.
BoxCars: Improving Fine-Grained Recognition of Vehicles Using 3-D Bounding Boxes in Traffic Surveillance
IEEE Transactions on Intelligent Transportation Systems ; 20 , 1 ; 97-108
2019-01-01
2708057 byte
Article (Journal)
Electronic Resource
English
DOOR ASSEMBLIES FOR INSULATED BOXCARS AND REFRIGERATED BOXCARS
European Patent Office | 2023
|Oil tankers, boxcars thrive on flux-cored welding
Tema Archive | 1975
Train of Frozen Boxcars Model for Fluidic Harvesters
AIAA | 2017
|British Library Conference Proceedings | 2020
|SAE Technical Papers | 2020
|