Mean shift and log-polar transform for road sign detection

Ellahyani, Ayoub; Ansari, Mohamed El

doi:10.1007/s11042-016-4207-3

Cited by 32 publications

(28 citation statements)

References 28 publications

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“…However, the performance of its method degrades when there exists low image contrast or various illumination conditions. Note that, the method in [37], [21] and [33] performs comparably with the method in [8], with a slight improvement. Because the dataset contains more mandatory road signs, the performance of the method in [23] drops slightly.…”

Section: Road Sign Detection Resultsmentioning

confidence: 89%

“…In order to demonstrate the superior performance of our proposed algorithm, we compare the performance of our algorithm with those of the state-of-the-art approaches on three benchmarking datasets and a newly collected dataset. These approaches include the SVM approach proposed in [26], the approach integrating object detection with 3D tracking in [37], the Fourier descriptors in [21], the ROI extraction and histogram features-based method in [23], the image segmentation and shape analysis method in [20], Mean shift and log-polar transform [8], fast R-CNN method [33] and the RST approach [24]. Detection rate (Hits), false alarm rate (FA) and average processing time are compared.…”

Section: Road Sign Detection Resultsmentioning

confidence: 99%

“…It is publicly available at: http://www.zemris.fer.hr/~ssegvic/mastif/datasets.shtml. [21] 95.1% 1.27% 2s Liang [23] 95.8% 1.03% 800mš Segvi [36] 95% 1.28% -Timofte [37] 94.7% 1.34% 286ms Ellahyani [8] 94.9% 1.3% 389ms Qian [33] 96% 1.27% 950ms [37] 95.6% 1.61% 3s Larsson [21] 95.4% 1.26% -Liang [23] 94.9% 1.28% 986mš Segvi [36] 92.9% 2.04% 451ms Ellahyani [8] 95.1% 1.74% 419ms Qian [33] 95.8% 1.39% 1.2s Table 2 shows the comparative results obtained on this dataset. As it shows, the results obtained using the method in [23] and [33] are very close to that of our algorithm, but the method in [23] and [33] run at a frame rate of 0.5 frames and 0.95 frames per second respectively, whereas our system runs at a frame rate of approximately 5 frames per second on this dataset.…”

Section: Road Sign Detection Resultsmentioning

confidence: 99%

“…Until very recently, quite a number of papers have been published on road sign detection. In [8], a two-step road sign detection method was proposed, which was robust to many kinds of adverse situations including poor lighting condition, partial occlusion, rotation, etc. However, the robustness of the method to occlusions still needs to be improved.…”

mentioning

confidence: 99%

“…Their work differs from previous work on adversarial sample crafting and defense against adversarial samples in a way that they are aggregating, applying and verifying the previous methods in the context of traffic signs. Ellahyani et al [9] proposed a new method for road sign detection by employing both a random forest classifier and a shape based classification technique. The proposed detection method has achieved satisfactory results.…”

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confidence: 99%

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Fast and robust road sign detection in driver assistance systems

2018

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Road sign detection plays a critical role in automatic driver assistance systems. Road signs possess a number of unique visual qualities in images due to their specific colors and symmetric shapes. In this paper, road signs are detected by a two-level hierarchical framework that considers both color and shape of the signs. To address the problem of low image contrast, we propose a new color segmentation algorithm based on color visual saliency, which uses the ratios of enhanced and normalized color values to capture color information. To improve computation efficiency and reduce false alarm rate, we modify the fast radial symmetry transform (RST) algorithm, and propose to use an edge pairwise voting scheme to group feature points based on their underlying symmetry in the candidate regions. Experimental results on several benchmarking datasets demonstrate the superiority of our method over the state-of-the-arts on both efficiency and robustness.

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Section: Road Sign Detection Resultsmentioning

confidence: 89%

Section: Road Sign Detection Resultsmentioning

confidence: 99%

Section: Road Sign Detection Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Fast and robust road sign detection in driver assistance systems

2018

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DeePOF: A hybrid approach of deep convolutional neural network and friendship to Point‐of‐Interest (POI) recommendation system in location‐based social networks

Safavi

Jalali

2022

Concurrency and Computation

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Today, millions of active users spend a percentage of their time on location-based social networks like Yelp and Gowalla and share their rich information. They can easily learn about their friends' behaviors and where they are visiting and be influenced by their style. As a result, the existence of personalized recommendations and the investigation of meaningful features of users and Point of Interests (POIs), given the challenges of rich contents and data sparsity, is a substantial task to accurately recommend the POIs and interests of users in location-based social networks (LBSNs). This work proposes a novel pipeline of POI recommendations named DeePOF based on deep learning and the convolutional neural network. This approach only takes into consideration the influence of the most similar pattern of friendship instead of the friendship of all users. The mean-shift clustering technique is used to detect similarity. The most similar friends' spatial and temporal features are fed into our deep CNN technique. The output of several proposed layers can predict latitude and longitude and the ID of subsequent appropriate places, and then using the friendship interval of a similar pattern, the lowest distance venues are chosen. This combination method is estimated on two popular datasets of LBSNs. Experimental results demonstrate that analyzing similar friendships could make recommendations more accurate and the suggested model for recommending a sequence of top-k POIs outperforms state-of-the-art approaches.

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Small Traffic Sign Detection and Recognition in High-Resolution Images

You

Wang

et al. 2019

Cognitive Computing – ICCC 2019

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Traffic sign detection and recognition is a research hotspot in the computer vision and intelligent transportation systems fields. It plays an important role in driver-assistance systems and driverless operation. Detecting signs, especially small ones, remains challenging under a variety of road traffic conditions. In this manuscript, we propose an end-to-end deep learning model for detecting and recognizing traffic signs in high-resolution images. The model consists of basic feature extraction and multi-task learning. In the first part, a network with fewer parameters is proposed, and an effective feature fusion strategy is adopted to gain a more distinct representation. In the second part, multi-task learning is conducted on different hierarchical layers by considering the difference between the detection and classification tasks. The detection results on two newly published traffic sign benchmarks (Tsinghua-Tencent 100K and CTSD) demonstrate the robustness and superiority of our model.

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Mean shift and log-polar transform for road sign detection

Cited by 32 publications

References 28 publications

Fast and robust road sign detection in driver assistance systems

Fast and robust road sign detection in driver assistance systems

DeePOF: A hybrid approach of deep convolutional neural network and friendship to Point‐of‐Interest (POI) recommendation system in location‐based social networks

Small Traffic Sign Detection and Recognition in High-Resolution Images

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