Vehicle model recognition from frontal view image measurements

Psyllos, Apostolos; Anagnostopoulos, Christos; Kayafas, E.

doi:10.1016/j.csi.2010.06.005

Cited by 123 publications

(58 citation statements)

References 7 publications

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“…Before implementing the classification, we firstly divide the dataset in [31] into two categories: large vehicle dataset and small vehicle dataset, where large vehicle dataset consists of two types of vehicles, bus and truck, and small vehicle dataset consists of three types of vehicles, passenger car, minivan, and sedan. Our method averagely achieves 96.3% classification accuracy on daylight images and 89.5% on nighttime images, better than the results of previous [32] 78.3% 73.3% Petrovic and Cootes [33] 84.3% 82.7% Peng et al [31] 90.0% 87.6% Dong and Jia [8] 91.3% -Dong et al [1] 96 methods, as demonstrated in Table 8. Additionally, we also test our method on the BIT-Vehicle dataset provided in [1]; our method achieves 90.1% classification accuracy, yet the accuracy of the method used in [1] reaches 88.11%.…”

Section: Comparison Of Results With Other Methodsmentioning

confidence: 57%

Vehicle Type Recognition Combining Global and Local Features via Two-Stage Classification

Sun

Zhang

Shi

et al. 2017

Mathematical Problems in Engineering

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This study proposes a new vehicle type recognition method that combines global and local features via a two-stage classification. To extract the continuous and complete global feature, an improved Canny edge detection algorithm with smooth filtering and non-maxima suppression abilities is proposed. To extract the local feature from four partitioned key patches, a set of Gabor wavelet kernels with five scales and eight orientations is introduced. Different from the single-stage classification, where all features are incorporated into one classifier simultaneously, the proposed two-stage classification strategy leverages two types of features and classifiers. In the first stage, the preliminary recognition of large vehicle or small vehicle is conducted based on the global feature via a -nearest neighbor probability classifier. Based on the preliminary result, the specific recognition of bus, truck, van, or sedan is achieved based on the local feature via a discriminative sparse representation based classifier. We experiment with the proposed method on the public and established datasets involving various challenging cases, such as partial occlusion, poor illumination, and scale variation. Experimental results show that the proposed method outperforms existing state-of-the-art methods.

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Section: Comparison Of Results With Other Methodsmentioning

confidence: 57%

Vehicle Type Recognition Combining Global and Local Features via Two-Stage Classification

Sun

Zhang

Shi

et al. 2017

Mathematical Problems in Engineering

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show abstract

“…Due to growing demand, other categories of vehicle classification have also been added recently. Make and model recognition (MMR) [59] and color recognition (CR) of cars are relatively new functionalities.…”

Section: Introductionmentioning

confidence: 99%

A smart camera for the surveillance of vehicles in intelligent transportation systems

Baran

Ruść

Fornalski

2015

Multimed Tools Appl

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The paper presents a smart camera aimed at security and law enforcement applications for intelligent transportation systems. An extended background is presented first as a scholar literature review. The smart camera components and their capabilities for automatic detection and recognition of selected parameters of cars, as well as different aspects of the system efficiency, are described and discussed in detail in subsequent sections. Smart features of make and model recognition (MMR), license plate recognition (LPR) and color recognition (CR) are highlighted as the main benefits of the system. Their implementations, flowcharts and recognition rates are described, discussed and finally reported in detail. In addition to MMR, three different approaches, referred to as bag-of-features, scalable vocabulary tree and pyramid match, are also considered. The conclusion includes a discussion of the smart camera system efficiency as a whole, with an insight into potential future improvements.

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“…Many other approaches appear in the literature for example, Huang at al's 2D-LDA approach [6], Psyllos et al [11] use symmetry measurements and Sarfraz et al [13] present a local energy based shape histogram to encode vehicle shape. When dealing with vehicle images at a wide variety of scales, SIFT-based approaches are popular [7].…”

Section: Related Workmentioning

confidence: 99%