Name
Title | ||
|---|---|---|
Optimized segmentation and multiscale emphasized feature extraction for traffic sign detection and recognition. |
Abstract | ||
|---|---|---|
Traffic sign detection and recognition has been a topic of research for at least the last two decades. Efforts are being made to reliably detect candidate traffic signs in natural uncontrolled environment and to recognize their contents. For detection, a large proportion of relevant literature discusses color based segmentation by either sticking to a predefined color space (e.g., RGB, HSI, YCbCr etc.) or make use of empirically selected subset of eigen space to achieve partially data dependent segmentation. Since, the input RGB data for various color classes and the background is not linearly separable, none of the existing methods guarantee to achieve complete separation among pixels corresponding to traffic signs and the background objects. To tackle this problem, we propose a completely data driven segmentation technique that adaptively selects an optimized color space based on available training data. To recognize the contents of potential traffic signs, we present a hybrid spatio-frequency radial feature extraction technique with an emphasis on the regions containing useful information. We explore the energy compaction property of steerable discrete cosine transform for feature extraction and augment it with well known circular histogram of oriented gradients in a pyramid. Using our proposed method, experiments on (1) German Traffic Sign Detection Benchmark, (2) our self collected dataset and on a (3) hand crafted version of the combination of the two provide competitive performance compared to various latest and state of the art methods by achieving up to 0.978 precision and 0.98 recall values at an expense of only an insignificant additional computational cost. The method also obtained 0.81 precision on traffic signs partially occluded by other objects. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.3233/JIFS-181082 | JOURNAL OF INTELLIGENT & FUZZY SYSTEMS |
Keywords | Field | DocType |
traffic sign,multiscale feature extraction,optimized color space,steered DCT,pyramidal HOG | Pattern recognition,Segmentation,Feature extraction,Artificial intelligence,Mathematics,Machine learning,Traffic sign detection | Journal |
Volume | Issue | ISSN |
36 | 1 | 1064-1246 |
Citations | PageRank | References |
0 | 0.34 | 24 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Abdul Mannan | 1 | 0 | 0.68 |
| Kashif Javed | 2 | 110 | 8.87 |
| Atta-ur-Rehman | 3 | 0 | 0.34 |
| Serosh Karim Noon | 4 | 0 | 0.34 |
| Haroon Atique Babri | 5 | 226 | 6.97 |