Adjacent LBP and LTP based background modeling with mixed-mode learning for foreground detection

Acharya, Sheetal; Nanda, Pradipta Kumar

doi:10.1007/s10044-021-00967-z

Cited by 11 publications

(1 citation statement)

References 45 publications

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“…Traffic sign recognition is often carried out in a complex outdoor environment, which is vulnerable to the interference of natural environment and human factors, such as bad weather, different light and shade, sign blocking and damage, etc., thus causing recognition difficulties [2] . Based on this, in the research of traffic sign recognition, a large number of complex algorithms have been proposed [3] Traditional algorithms mainly rely on artificial feature extraction, such as local binary pattern (LBP) [4] , Gabor [5] , histogram of oriented gradient (HOG) [6] , etc., and use support vector machine (SVM) [7] , AdaBoost [8] and other classifiers to complete traffic sign recognition. However, in the face of complex outdoor environment, artificial feature extraction can not meet the actual needs.…”

Section: A Literature Reviewmentioning

confidence: 99%

An Improved Light-Weight Traffic Sign Recognition Algorithm Based on YOLOv4-Tiny

et al. 2021

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Aiming at the problems of low detection accuracy and inaccurate positioning accuracy of light-weight network in traffic sign recognition task, an improved light-weight traffic sign recognition algorithm based on YOLOv4-Tiny was proposed. By improving the K-means clustering algorithm, the anchor with appropriate size is generated for the traffic sign data set to improve the detection recall rate and target positioning accuracy. The strategy of large-scale feature map optimization is proposed, which enriches the feature level of the network by using the low-level information, strengthens the representation of the feature information of the small target, and improves the detection accuracy of the long-range small target. In view of the problem of missed detection of high overlapping targets in the post-processing stage of the model, the paper proposes an improved NMS algorithm to screen the prediction box, avoid deleting the prediction results of different targets, and further improve the detection accuracy and recall rate of the target. Experimental results show that, compared with the original YOLOv4-Tiny algorithm, the improved algorithm in traffic sign recognition task based on TT100K dataset, mAP and recall are improved by 5.73% and 7.29% respectively, and FPS value is maintained at about 87 f/s, which meets the accuracy and realtime requirements of traffic sign recognition task.INDEX TERMS traffic sign recognition, YOLOv4-Tiny, clustering algorithm, large scale, improved NMS algorithm

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