# A New Real-Time Detection and Tracking Method in Videos for Small Target Traffic Signs
## 2 Materials and Methods  
### Fine-Grained Classifications and Sample Equalizations  
Fine-grained image classification [45] is a challenging problem aiming to differentiate subordinate classes of a typical superior class. The purpose of the paper is to distinguish between superior and subordinate classes. There are two difficulties of fine-grained classification for traffic signs. Similarly, on the other hand, there is little difference between those of the same subordinate classes in the superior class. As shown in Figure 1a, the four symbols are all signs of prohibition, but the differences in characteristics are not significant. On the other hand, some specific traffic signs are shown in Figure 1b which reveal large differences between the same subordinate categories, such as height, width, speed limit, etc. Thus, traffic signs are more difficult to detect, and especially when self-driving cars are involved. Unfortunately, TT100K has an unbalanced proportion of traffic signs.  
As shown in Figure 2 (the blue part), nearly half of the traffic sign categories in the dataset have very few samples, which can easily cause insufficient deep learning training, and eventually result in incorrect detection of small sample signs. [PERSON] et al. [46] proposed few-shot learning that addressed the problem of detection caused by the insufficient training set or large training set but insufficient labeling information such as face detections and cell classifications. However, promising results are seldom if the dataset is combined with small or large samples added to it. Moreover, the primary detection object is small targets, which lead to even more undesirable results. According to the Vicinal Risk Minimization (VRM) principle, the generalization capacity should be improved by creating samples similar to the training samples for data augmentation [47]. Therefore, data augmentation is employed for small sample traffic signs in the TT100K to achieve the purpose of sample equilibrium, which is inspired by [48]. The details are as follows: 15 methods of data enhancement (e.g., noise enhancement, weather enhancement, fuzzy enhancement, and data enhancement). Finally, this paper applies these 15 types of data enhancement to the TT100K benchmark dataset, including Gaussian Noise, Shot Noise, Impulse Noise, Defocus Blur, Frosted Glass Blur, Motion Blur, Zoom Blur, Snow, Frost, Fog, Brightness, Contrast, Elastic, Pixelate, and JPEG.  
The comparison of the distribution of various traffic signs between the TT100K and the enhanced TT100K is shown in Figure 2. After data augmentation, the number of small  
Figure 1: (**a**) Some examples of prohibitory signs. (**b**) Specific traffic signs, including height, width, and the speed limit.  
Figure 2: Comparison diagram of TT100K and enhanced TT100K traffic sign category distribution.  
samples has definitely increased, as well as the category distribution of traffic signs has improved. Most of the training data contains a mix of signs, so augmenting smaller sets of data would necessitate data augmentation for the large category as well.