# A New Real-Time Detection and Tracking Method in Videos for Small Target Traffic Signs
## 1 Introduction  
Traffic sign detection and tracking is a critical task of self-driving vehicles in Real-World traffic scenarios, which provides real-time decision support for the autopilot system. Traffic sign detection can be broadly divided into two categories [1, 2, 3, 4, 5, 6]. One is the traditional method based on manual features [1, 2, 3, 4, 5], and the other is the deep learning algorithm based on CNN (Convolutional Neural Network) [6]. Traditional traffic signs are mainly detected based on the appearance characteristics of some traffic signs. In [2, 3], RGB (Red, Green, Blue) and HSI (Hue, Saturation, Intensity) color model methods are used for detection owing to different color information (red, yellow, blue) for multifarious traffic signs. In [4], HOG (Histogram of Gradient) is employed to describe shape features used for detection. However, due to the small target of traffic signs, they are easily affected by external adverse factors such as lighting, weather, and shielding [5]. It is necessary to use nontraditional algorithms to extract features such as color, texture, context, etc. in order to have higher commonality. In contrast, deep learning-based object detection algorithms are more accurate and capable of evolving to more complex environments [6]. CNN-based object detection methods can be further divided into two types [7, 8, 9, 10, 11, 12, 13, 14]: two-stage schemes and one-stage schemes, where the two-stage schemes combine RPN (Region Proposal Network) with the CNN network at first, then classify and regress thesecandidate regions [7] such as R-CNN [8] (Region-Convolutional Neural Network), Fast R-CNN [9], Mask R-CNN [10], etc. Even though it is possible to achieve a high detection accuracy with the two-step schemes, it is demanding because of its complicated computation. Two-stage schemes are slower at detection, but end-to-end ones provide more accurate results. SSD (Single Shot Detector) [11], YOLO [12], YOLO 9000 [13], YOLOv3 [14], etc. are typical representatives of one-stage schemes. Because of the progress of deep learning, there have been new deep learning-based object detection algorithms that have progressively released. [PERSON] et al. [15] proposed a new traffic sign detection method. They used a two-stage strategy to extract region proposals and introduced AN (Attention Network), combining Faster R-CNN with traditional computer vision algorithms to find regions of interest by using color characteristics. Finally, the experimental results showed that its mAP was 80.31%. [PERSON] et al. [16] improved the detection effect of Faster R-CNN by introducing a visual attention model which can integrate a series of regions for locating and classifying small objects. Their mAP is 87.0%, and the efficiency is 3.85 FPS. The detection algorithms mentioned above have achieved excellent detection results on the dataset PASCAL VOC (Pattern Analysis Statistical Modelling and Computational Learning Visual Object Classes) [17] and COCO (Common Objects in Context) [18]. In [19], good performance on the most commonly used traffic sign dataset GTSDB (German Traffic Sign Detection Benchmark) [20] has been achieved. The improved YOLOv2 achieved 99.61% and 96.69% precision in CCTSDB (CSUST Chinese Traffic Sign Detection Benchmark) and GTSDB [21]. At the same time, however, only classifying all traffic signs as detectable or non-detectable (i.e., prohibitory signs, command, and notification signs) is a fair categorization due to the great disparities between detection types and their corresponding traffic signs. It is far from meeting the actual scenario requirements in the self-driving task. The TT100K (Tsinghua-Tencent 100 K) [22] benchmark dataset subdivides the three types of traffic signs into 128 categories, covering varieties of factors under different light conditions and climatic conditions, which is closer to the Real-World traffic scenarios, and it also contains more backgrounds and smaller targets.