Vehicle Fine-grained Recognition Based on Convolutional Neural Networks for Real-world Applications

Špaňhel, Jakub; Sochor, Jakub; Makarov, Aleksej

doi:10.1109/neurel.2018.8587012

Cited by 6 publications

(4 citation statements)

References 21 publications

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“…The imagery these cameras provide can be a further source of data when combined with machine learning and object detection. Through these, it is possible to obtain data for the modeling of traffic and pedestrian flows, as part of micro- and macro-simulation systems, ranging from the discrete movements and interactions of a single entity to overall flow characteristics on a regional scale by simultaneously analyzing conditions across multiple camera sensors, respectively [ 1 , 2 , 3 , 4 ]. Recent years have seen considerable research focus on improved machine learning for CCTV applications, and a range of open-source, research, and commercial implementations are now widespread.…”

Section: Introductionmentioning

confidence: 99%

Estimating Vehicle and Pedestrian Activity from Town and City Traffic Cameras

Chen

Grimstead

Bell

et al. 2021

Sensors

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Traffic cameras are a widely available source of open data that offer tremendous value to public authorities by providing real-time statistics to understand and monitor the activity levels of local populations and their responses to policy interventions such as those seen during the COrona VIrus Disease 2019 (COVID-19) pandemic. This paper presents an end-to-end solution based on the Google Cloud Platform with scalable processing capability to deal with large volumes of traffic camera data across the UK in a cost-efficient manner. It describes a deep learning pipeline to detect pedestrians and vehicles and to generate mobility statistics from these. It includes novel methods for data cleaning and post-processing using a Structure SImilarity Measure (SSIM)-based static mask that improves reliability and accuracy in classifying people and vehicles from traffic camera images. The solution resulted in statistics describing trends in the ‘busyness’ of various towns and cities in the UK. We validated time series against Automatic Number Plate Recognition (ANPR) cameras across North East England, showing a close correlation between our statistical output and the ANPR source. Trends were also favorably compared against traffic flow statistics from the UK’s Department of Transport. The results of this work have been adopted as an experimental faster indicator of the impact of COVID-19 on the UK economy and society by the Office for National Statistics (ONS).

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Section: Introductionmentioning

confidence: 99%

Estimating Vehicle and Pedestrian Activity from Town and City Traffic Cameras

Chen

Grimstead

Bell

et al. 2021

Sensors

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“…As an important part of smart city field [6,7], object recognition based on computer vision has attracted much attention. Specifically, fine-grained image classification has been widely used in vehicle type recognition [8][9][10], goods recognition [11], content-based image retrieval [12], and other smart city applications [13][14][15][16][17]. In these applications, recognizing fine-grained images is still challenging, due to the high similarity between images in the same categories and the high dissimilarity in the same subcategories caused by different poses, behaviors, and so on as shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Knowledge Graph Representation Fusion Framework for Fine‐Grained Object Recognition in Smart Cities

Tian

Zhang

et al. 2021

Complexity

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Autonomous object detection powered by cutting-edge artificial intelligent techniques has been an essential component for sustaining complex smart city systems. Fine-grained image classification focuses on recognizing subcategories of specific levels of images. As a result of the high similarity between images in the same category and the high dissimilarity in the same subcategories, it has always been a challenging problem in computer vision. Traditional approaches usually rely on exploring only the visual information in images. Therefore, this paper proposes a novel Knowledge Graph Representation Fusion (KGRF) framework to introduce prior knowledge into fine-grained image classification task. Specifically, the Graph Attention Network (GAT) is employed to learn the knowledge representation from the constructed knowledge graph modeling the categories-subcategories and subcategories-attributes associations. By introducing the Multimodal Compact Bilinear (MCB) module, the framework can fully integrate the knowledge representation and visual features for learning the high-level image features. Extensive experiments on the Caltech-UCSD Birds-200-2011 dataset verify the superiority of our proposed framework over several existing state-of-the-art methods.

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“…In recent years, the development of technology in the field of computer vision and the breakthrough of technology in the field of Internet of Things promote the realization of smart city concept [1]. As important objects in smart city applications, vehicles have attracted extensive attention, a lot of researches about vehicles has been carried out, such as vehicle detection [2], [3], vehicle tracking [4], [5], finegrained vehicle type recognition [6]- [8], etc. As a frontier and important research topic, vehicle re-identification also caused more and more attention in research area, the purpose of vehicle re-identification is to identify the same vehicle through multiple non-overlapping cameras [9], as shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

A Survey of Vehicle Re-Identification Based on Deep Learning

2019

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Vehicle re-identification is one of the core technologies of intelligent transportation systems, and it is crucial for the construction of smart cities. With the rapid development of deep learning, vehicle re-identification technologies have made significant progress in recent years. Therefore, making a comprehensive survey about the vehicle re-identification methods based on deep learning is quite indispensable. There are mainly five types of deep learning-based methods designed for vehicle re-identification, i.e. methods based on local features, methods based on representation learning, methods based on metric learning, methods based on unsupervised learning, and methods based on attention mechanism. The major contributions of our survey come from three aspects. First, we give a comprehensive review of the current five types of deep learning-based methods for vehicle re-identification, and we further compare them from characteristics, advantages, and disadvantages. Second, we sort out vehicle public datasets and compare them from multiple dimensions. Third, we further discuss the challenges and possible research directions of vehicle re-identification in the future based on our survey. INDEX TERMS Deep learning, intelligent transportation system, vehicle re-identification, vehicle public datasets.

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Vehicle Fine-grained Recognition Based on Convolutional Neural Networks for Real-world Applications

Cited by 6 publications

References 21 publications

Estimating Vehicle and Pedestrian Activity from Town and City Traffic Cameras

Estimating Vehicle and Pedestrian Activity from Town and City Traffic Cameras

Knowledge Graph Representation Fusion Framework for Fine‐Grained Object Recognition in Smart Cities

A Survey of Vehicle Re-Identification Based on Deep Learning

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