Detection, Tracking and Classification of Road Signs in Adverse Conditions

Σιόγκας, Γεώργιος; Dermatas, Evangelos

doi:10.1109/melcon.2006.1653157

Cited by 53 publications

(26 citation statements)

References 18 publications

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“…For the classification step, well-known machine learning techniques, such as neural network (NN) [7], linear discriminative analysis (LDA) [1], SURF matching [3], and cross-correlation [17] have been applied. While Eichner and Breckon [7] tried to classify the context of speed limit signs, others tried to classify the appearances of the signs, e.g., color and shape information, rather than their contexts.…”

Section: Related Workmentioning

confidence: 99%

“…The majority of the tracking systems deal with the dynamics of the tracked signs while the others cope with the appearances of the tracked signs. In order to reduce the search region and improve the detection performance, the discrete-time dynamics of vehicles have been utilized, for example, in Bayesian Filters [8], [11], [13], [14], [17] and information fusion [1], in order to predict the position of a sign in subsequent frames.…”

Section: Related Workmentioning

confidence: 99%

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Kernel-based traffic sign tracking to improve highway workzone recognition for reliable autonomous driving

Lee

Seo

Zhang

et al. 2013

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)

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Abstract-To be deployed in the real world, a self-driving car must be capable of responding to exceptional road conditions, such as temporary work zones, because such events can change previously known traffic rules and road geometry. To develop such a capability, we implemented a computer vision system that recognizes the bounds of highway workzones by detecting regulatory and warning workzone signs. Because it is not practical to expect perfect performance in sign recognition, we also developed a confidence-propagation method to handle potential sign recognition errors. The performance of highway workzone recognition was improved by confidence-propagation, but our approach is not easily scalable to some real-world scenarios. Instead of only propagating sign classification confidence, in this work we project the appearance information of previously detected signs onto the current frame, to constrain the region for searching. Through experiments, we show that kernel-based tracking reduced the miss and false detection rates, result in a better performance of highway workzone recognition. In this paper, we present our on-going effort to further improve the performance of our highway workzone recognition system.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Kernel-based traffic sign tracking to improve highway workzone recognition for reliable autonomous driving

Lee

Seo

Zhang

et al. 2013

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)

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show abstract

“…Among those systems, we divide them into two different approaches: one deals with the dynamics of the tracked signs, and the other copes with the appearance of the tracked signs. The dynamics approach for tracking is to utilize the discrete-time dynamics of vehicles, such as with Bayesian Filters [6], [8], [10], [11], [14] and information fusion [1] to predict the position of a sign in subsequent frames based on image frames received previously. This can reduce the computational cost, while still only relying on the detection performances, if the update model is defined by only the detection algorithm.…”

Section: Related Workmentioning

confidence: 99%

Kernel-based tracking for improving sign detection performance

Lee

Seo

Wettergreen

2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-To be deployed in the real-world, automatic and semi-automatic systems should understand traffic rules by recognizing and comprehending contents of traffic signs, because traffic signs inform what driving behaviors should be. In this paper, we present the successful application of methods to improve the traffic sign localization performance. Given a potential sign region, our algorithm represents both the detected sign as a target and candidates in the subsequent frame as probability density functions. Then, our algorithm maximizes the similarity between a target and candidates to localize the sign. Finally, the maximum similarity among candidates is assigned as a tracked sign. The experimental results verify that our algorithm can robustly localize traffic signs in images under various weather conditions and driving scenarios.

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“…Detection stage is very important because the road signs that are not detected during this step is not available to be recovered later. Classification shall be conducted using traditional template matching (Siogkas and Dermatas, 2006) or via techniques from the field of machine learning, such as Support Vector Machines (Maldonado-Bascon et al, 2007;Adam and Ioannidis, 2014) and deep learning (Sermanet and LeCun, 2012;Ciresan et al, 2012). Especially, Ciresan et al, (2012) presented a state-of-the-art road sign classification approach using deep neural network, which won the German * Corresponding author traffic sign recognition benchmark with a recognition rate of 99.46%, better than the one of humans on this task.…”

Section: Introductionmentioning

confidence: 99%

Road Signs Detection and Recognition Utilizing Images and 3d Point Cloud Acquired by Mobile Mapping System

Li¹,

Shinohara²,

Satoh³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:High-definition and highly accurate road maps are necessary for the realization of automated driving, and road signs are among the most important element in the road map. Therefore, a technique is necessary which can acquire information about all kinds of road signs automatically and efficiently. Due to the continuous technical advancement of Mobile Mapping System (MMS), it has become possible to acquire large number of images and 3d point cloud efficiently with highly precise position information. In this paper, we present an automatic road sign detection and recognition approach utilizing both images and 3D point cloud acquired by MMS. The proposed approach consists of three stages: 1) detection of road signs from images based on their color and shape features using object based image analysis method, 2) filtering out of over detected candidates utilizing size and position information estimated from 3D point cloud, region of candidates and camera information, and 3) road sign recognition using template matching method after shape normalization. The effectiveness of proposed approach was evaluated by testing dataset, acquired from more than 180 km of different types of roads in Japan. The results show a very high success in detection and recognition of road signs, even under the challenging conditions such as discoloration, deformation and in spite of partial occlusions.

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Detection, Tracking and Classification of Road Signs in Adverse Conditions

Cited by 53 publications

References 18 publications

Kernel-based traffic sign tracking to improve highway workzone recognition for reliable autonomous driving

Kernel-based traffic sign tracking to improve highway workzone recognition for reliable autonomous driving

Kernel-based tracking for improving sign detection performance

Road Signs Detection and Recognition Utilizing Images and 3d Point Cloud Acquired by Mobile Mapping System

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