Image Shadow Detection and Removal in Autonomous Vehicle Based on Support Vector Machine

Zhu, Tianjun; Yin, Xiaoxuan

doi:10.18494/sam.2020.2788

Cited by 3 publications

(3 citation statements)

References 19 publications

(20 reference statements)

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“…A shadow detection method proposed by Zhu et al [57] uses SVM to train an RGB image with shadow which is then divided into shadow and light regions. The light intensity in shadow region was adjusted by elimination of pixel difference between both regions.…”

Section: ) Statistical Approaches: Non-parametricmentioning

confidence: 99%

A Comprehensive Review of Vehicle Detection Techniques Under Varying Moving Cast Shadow Conditions Using Computer Vision and Deep Learning

et al. 2022

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Design of a vision-based traffic analytic system for urban traffic video scenes has a great potential in context of Intelligent Transportation System (ITS). It offers useful traffic-related insights at much lower costs compared to their conventional sensor based counterparts. However, it remains a challenging problem till today due to the complexity factors such as camera hardware constraints, camera movement, object occlusion, object speed, object resolution, traffic flow density, and lighting conditions etc. ITS has many applications including and not just limited to Queue estimation, Speed detection and different anomalies detection etc. All of these applications are primarily dependent on sensing vehicle presence to form some basis for analysis. Moving Cast shadows of vehicles is one of the major problems that affects the vehicle detection as it can cause detection and tracking inaccuracies. Therefore, it is exceedingly important to distinguish dynamic objects from their moving cast shadows for accurate vehicle detection and recognition. This paper provides an in-depth comparative analysis of different traffic paradigm-focused conventional and state-of-the-art shadow detection and removal algorithms. Till date, there has been only one brief survey which highlights the shadow removal methodologies particularly for traffic paradigm. In this paper, a number of research papers containing results of urban traffic scenes have been shortlisted from the last three decades to give a comprehensive overview of the work done in this area. The study reveals that the only way to make a comparative evaluation is to use the existing Highway I, II, and III datasets which are frequently used for qualitative or quantitative analysis of shadow detection or removal algorithms. Furthermore, the paper not only provides cues to solve moving cast shadow problems, but also suggests that even after the advent of Convolutional Neural Network (CNN)-based vehicle detection methods, the problems caused by moving cast shadows persists. Therefore, this paper proposes a hybrid approach which uses a combination of conventional and state-of-the-art techniques as a pre-processing step for shadow detection and removal before using CNN for vehicles detection. The results indicate a significant improvement in vehicle detection accuracies after using the proposed approach.

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Section: ) Statistical Approaches: Non-parametricmentioning

confidence: 99%

A Comprehensive Review of Vehicle Detection Techniques Under Varying Moving Cast Shadow Conditions Using Computer Vision and Deep Learning

et al. 2022

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“…In this study, the semantic image segmentation based on the DeepLab V3+ model was used to extract the urban façade. Next, the image shadow detection method based on the CIELAB color space was used to detect shadow areas [36][37][38]. The identified shadow area was removed from the image of the urban façade to mitigate the effect of shadow on the extraction of the DCUF.…”

Section: Extraction Of the Urban Façade From Bsvmentioning

confidence: 99%

“…The distortion of image color information will ultimately lead to errors in the extraction of dominant colors. An image shadow detection method was adopted based on the CIELAB color space to eliminate the influence of shadows on the extraction results of DCUF [38,43]. There are three color channels in the CIELAB color space, namely L, A, and B.…”

Section: Shadow Detection Of the Urban Façade Using Cielab Color Spacementioning

confidence: 99%

City-Scale Mapping of Urban Façade Color Using Street-View Imagery

et al. 2021

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Precise urban façade color is the foundation of urban color planning. Nevertheless, existing research on urban colors usually relies on manual sampling due to technical limitations, which brings challenges for evaluating urban façade color with the co-existence of city-scale and fine-grained resolution. In this study, we propose a deep learning-based approach for mapping the urban façade color using street-view imagery. The dominant color of the urban façade (DCUF) is adopted as an indicator to describe the urban façade color. A case study in Shenzhen was conducted to measure the urban façade color using Baidu Street View (BSV) panoramas, with city-scale mapping of the urban façade color in both irregular geographical units and regular grids. Shenzhen’s urban façade color has a gray tone with low chroma. The results demonstrate that the proposed method has a high level of accuracy for the extraction of the urban façade color. In short, this study contributes to the development of urban color planning by efficiently analyzing the urban façade color with higher levels of validity across city-scale areas. Insights into the mapping of the urban façade color from the humanistic perspective could facilitate higher quality urban space planning and design.

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DriveGuard: Robustification of Automated Driving Systems with Deep Spatio-Temporal Convolutional Autoencoder

Papachristodoulou¹,

Kyrkou²,

Theocharides³

2021

2021 IEEE Winter Conference on Applications of Computer Vision Workshops (WACVW)

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Image Shadow Detection and Removal in Autonomous Vehicle Based on Support Vector Machine

Cited by 3 publications

References 19 publications

A Comprehensive Review of Vehicle Detection Techniques Under Varying Moving Cast Shadow Conditions Using Computer Vision and Deep Learning

A Comprehensive Review of Vehicle Detection Techniques Under Varying Moving Cast Shadow Conditions Using Computer Vision and Deep Learning

City-Scale Mapping of Urban Façade Color Using Street-View Imagery

DriveGuard: Robustification of Automated Driving Systems with Deep Spatio-Temporal Convolutional Autoencoder

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