# A New Real-Time Detection and Tracking Method in Videos for Small Target Traffic Signs
## 1 Introduction
Although [23; 24] have achieved better detection accuracy on TT100K, their real-time performances are poor. In [25], the real-time problem is well addressed with a less-parameter and low-computational cost model MF-SSD, but its performance on small object detection is poor. [PERSON] et al. [26] proposed a Perceptual GAN (Generative Adversarial Network) model to improve the detection of small traffic signs in the TT100K by minimizing the differences between large and small objects. [PERSON] et al. [19] proposed a multiscale cascaded R-CNN and multiscale features in pyramids that fuse high-level semantic information and low-level spatial information. Moreover, the features extracted from the ROI (Region of Interest) for each scale are fined and then fused to improve the detection accuracy of traffic signs. Precision and recall achieved 98.7% and 90.5% respectively. Nevertheless, they only roughly divided the small traffic signs into three types. Obviously, it is not enough in practice. Actually, road object detection has reached a bottleneck for further improvement because of the small scale of targets [27; 28]. Infrared (IR) small object detection has been established [29] recently, as well as the remote sensing radar [30] and LiDAR [31]. Ref. [32] uses infrastructure-based LiDAR sensors for the detection and tracking of both pedestrians and vehicles at intersections and obtains good results on small objects. However, most of the above-mentioned detection methods are expensive which strongly limit their deployment in practical applications [33], and their deployment in day-to-day use is currently impeded by a number of unsolved problems: (1) Low detection accuracy of small traffic signs in large images. Compared with medium and large objects, small traffic signs lack appearance information needed to be distinguished from the background or other similar categories [34]. (2) The prior achievements in detecting small objects can't be verified since the vast majority of the research efforts are focused on large object detection [35]. Besides, due to the extremely uneven distribution of different traffic signs, it is generally easy to result in the problem of low recognition rate in those very-low-frequency samples. It is difficult but necessary to ensure high accuracy and robustness in traffic sign detection at the same time, especially in videos [36]. (3) Efficient simultaneous detection and tracking in videos. Owing to the lighting and weather interference in the real traffic environment and the onboard camera motion blur, bumps, etc. in the video detection [37; 38], the bounding box is prone to flickering and missing targets [39], resulting in missed detections and false detections [40]. The safety of self-driving vehicles may probably be threatened. Hence, this paper proposes an improved YOLV3 algorithm to help minimize the problems associated with small traffic signs and increase the overall YOLV3 performance. Furthermore, motivated from MOT (Multi-Object Tracking) [41], which is widely used to mark and track vehicles and pedestrians in videos in traffic surveillance systems and noisy crowd scenes most recently [42; 43]. Deep-Sort (Simple Online and Real-time Tracking) [44] is adopted to overcome a series of adverse factors brought by camera motion to real-time video detection. Compared to several latest detection methods, the proposed method has higher accuracy and real-time performance and meets the requirements of small target traffic signs detection. The main contributions of this paper are summarized as follows:  
(1) To address the problem of low detection accuracy resulted from exceptionally unbalanced samples distribution of different traffic signs in the TT100K, several image enhancement methods, such as adding noise, snow, frost, fog, blur, and contrast, etc., are applied to those categories of traffic signs that rarely appeared.