Online vehicle logo recognition using Cauchy prior logistic regression

Chen, Ruilong; Hawes, Matthew; Isupova, Olga; Mihaylova, Lyudmila; Zhu, Hao

doi:10.23919/icif.2017.8009720

Cited by 6 publications

(6 citation statements)

References 16 publications

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“…There are many keypoint-based methods for VLD including Local Binary Patterns (LBP) [12], Scale-Invariant Feature Transform (SIFT) [13][14][15][16], Speeded Robust Features (SURF) [17], Binary descriptor based on BRIEF (ORB) and Histograms of Oriented Gradients (HOG) method [18,19]. Those methods have been studied as features to represent the vehicle logo.…”

Section: Keypoint-based Methodsmentioning

confidence: 99%

Vehicle logo detection using an IoAverage loss on dataset VLD100K-61

Shi

Shen

et al. 2023

J Image Video Proc.

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Vehicle Logo Detection (VLD) is of great significance to Intelligent Transportation Systems (ITS). Although many methods have been proposed for VLD, it remains a challenging problem. To improve the VLD accuracy, an Intersection over Average (IoAverage) loss is proposed for enhancing the bounding box regression. The IoAverage loss accelerates the convergence of bounding box regression than using the Intersection over Union (IoU) loss. In the experiments, IoAverage loss has been incorporated into the state-of-the-art object detection framework YOLOV5s, namely YOLOV5s-IoAv in this paper. The advantages of the IoAverage loss are verified on the PASCAL VOC2007 datasets. The results of using the IoAverage loss show performance gains of + 15.27% mAP0.5 and + 30.87% mAP0.5:0.95 higher than that of the Complete IoU (CIoU) loss. The application of YOLOV5s-IoAv is implemented to VLD on dataset VLD100K-61. VLD100K-61 is a self-collected dataset containing 100,041 images supplied by traffic surveillance cameras in the real world from 61 categories. YOLOV5s-IoAv achieves performance gains as + 15.27% mAP0.5:0.95 for VLD than YOLOV5s-CIoU. The proposed method yields the mAP0.5 value of up to 0.992 on the dataset VLD100K-61, providing a promising solution to vehicle logo recognition applications.

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Section: Keypoint-based Methodsmentioning

confidence: 99%

Vehicle logo detection using an IoAverage loss on dataset VLD100K-61

Shi

Shen

et al. 2023

J Image Video Proc.

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“…Several popular handcrafted descriptors such as Scale-Invariant Feature Transform (SIFT) [4]- [6], Histogram of Oriented Gradients (HOG) [7], [8], Local Binary Patterns (LBP) [9], and their variations have been exploited for VLR tasks. Ou et al [10] proposed a VLR framework that first uses an AdaBoost-based detector to obtain the regions of interest (ROIs) and then utilized the SIFT descriptor for recognition.…”

Section: A Vlr Methods Based On Handcrafted Descriptorsmentioning

confidence: 99%

A Multilayer Pyramid Network Based on Learning for Vehicle Logo Recognition

Wang

et al. 2021

IEEE Trans. Intell. Transport. Syst.

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In this paper, we present a novel learning-based scheme for vehicle logo recognition (VLR). This scheme is termed Multilayer Pyramid Network Based on Learning (MLPNL) and is based on the principle that considering multiple resolutions is helpful for extracting valuable features that benefit the final recognition performance. The innovations of this scheme include (1) a multilayer pyramid network, with pixel difference matrices (PDMs) as its input and output and feature parameters mapping one PDM to another; (2) an objective function and a corresponding optimization method designed to facilitate the learning of the feature parameters of the proposed multilayer pyramid network; and (3) a multi-codebook-based encoding method that makes best use of the features extracted from PDMs corresponding to different resolutions. Extensive experiments conducted with an open dataset, HFUT-VL, demonstrate that the proposed MLPNL scheme outperforms state-of-the-art handcrafted descriptors and non-deep-learning-based learning methods when fewer training samples exist. Experiments conducted with a benchmark dataset, XMU, demonstrate that MLPNL outperforms existing state-ofthe-art VLR methods. Experiments conducted both on HFUT-VL and XMU demonstrate that MLPNL is faster than most deep-learning-based learning methods while maintaining nearly the same recognition rate. Code has been made available at: https://github.com/HFUT-CV/MLPNL.

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“…However, the identification of a vehicle is not always a straightforward procedure. During the last decades, several vehicle properties such as model [1], license plate [2], type [3] and logo [4] have been the core research objects in this domain. Still, most of these properties are not always fully apparent due to occlusions and problematic viewpoint angle; thus are difficult to discern in some contexts due to noise, i.e.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Color Identification Framework using Pixel-level Color Estimation from Segmentation Masks of Car Parts

Stavrothanasopoulos

Gkountakos

Ioannidis

et al. 2022

2022 IEEE 5th International Conference on Image Processing Applications and Systems (IPAS)

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Color comprises one of the most significant and dominant cues for various applications. As one of the most noticeable and stable attributes of vehicles, color can constitute a valuable and reliable key component in several practices of intelligent surveillance systems. In this paper, we propose a deep-learning-based framework that combines semantic segmentation masks with pixels clustering for automatic vehicle color recognition. Different from conventional methods, which usually consider only the features of the vehicle's front side, the proposed algorithm is able for view-independent color identification, which is more effective for the surveillance tasks. To the best of our knowledge, this is the first work that employs semantic segmentation masks along with color clustering for the extraction of the vehicle's color representative parts and the recognition of the dominant color, respectively. In order to evaluate the performance of the proposed method, we introduce a challenging multi-view dataset of 500 car-related RGB images extending the publicly available DSMLR Car Parts dataset for vehicle parts segmentation. The experiments demonstrate that the proposed approach achieves excellent performance and accurate results. To facilitate further research, the evaluation dataset and the pretrained models will be released at https://github.com (URL will be provided after the publication acceptance).

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Online vehicle logo recognition using Cauchy prior logistic regression

Cited by 6 publications

References 16 publications

Vehicle logo detection using an IoAverage loss on dataset VLD100K-61

Vehicle logo detection using an IoAverage loss on dataset VLD100K-61

A Multilayer Pyramid Network Based on Learning for Vehicle Logo Recognition

Vehicle Color Identification Framework using Pixel-level Color Estimation from Segmentation Masks of Car Parts

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