## 2 Related Work  
Because of the non-uniformity of vehicle taillight shapes and positions as well as their relatively small outline sizes, it is a challenge to detect and recognize the taillight semantic under real urban road environment condition in daytime. Therefore, there are relatively few studies on taillight detection and light semantic recognition in daytime urban traffic. Although, more interference and complex situations exist on the road during day, there is an advantage that the vehicle outline can be located clearly. Based on that outline, the taillight outline can be determined. In general, algorithms of taillights detection can be divided into the following categories: feature-based methods, machine-learning-based methods, and multi-sensor fusion methods. (1) Feature-based methods  
[PERSON] et al. used chain coding to maintain the original accuracy in the process of taillight outline detection and analyze the geometric rules of the taillight outline. They used virtual symmetry detection technology to locate the taillight position [7]. Based on the cognitive theory, [PERSON] et al. decomposed a video input stream into color, shape and other features which are closely combined with an image model and created the pixel values of interest in taillight areas according to the characteristics of taillights and atmospheric effects caused by external lighting and weather conditions [8]. [PERSON] et al. analyzed the taillight area color and brightness to find the invariant characteristics of the light scattering areas. Then, they used a classifier to train its dynamic frequency response and the size of the light scattering region to detect the taillight's brake light signal. However, this method did not consider the influence of illumination variation on light scattering in the taillight area [9]. Some scholars have extracted the correct taillight status by calculating the correlation of regional brightness values of the possible taillight areas of the vehicles on the road. They used the codebook theory to realize the all-weather tracking and detection of taillights, and the detection and identification of brake lights, left-turn lights, and right-turn lights [10; 11; 12]. The algorithm based on a priori information uses a relatively specific feature to detect the taillight. The advantage of this method is that it is faster to detect and can be applied to detection in a dynamic background. However, the disadvantage is that the relatively low level of the feature leads to a decrease in the ability to distinguish the target, which leads to an increase in false detection. (2) Machine learning-based methods  
[PERSON] et al. selected 8 directions and 5 different scales of Gabor filters to extract features of the vehicle taillight images and trained a BP neural network to extract the taillight distribution characteristics [13]. This method needs to further improve the matching effect of the taillights and solve the problem of poor detection of the red vehicles. [PERSON] et al. used a vehicle's rear appearance image to learn \"brake light mode\" through a multi-layer perceptron neural network in a large database and trained a depth classifier to judge whether the taillight was in the normal or braking state [14]. [PERSON] et al. proposed a method to optimize the faster RCNN for vehicle detection by improving multi shapes receptive field generation, anchor generation optimization and ROI assignment to improve detection speed and accuracy [15]. [PERSON] et al. proposed a single deep neural network for vehicle detection in urban traffic surveillance. Different feature extractors for target classification were used in this article, which improves detection speed and accuracy. The feature pyramid was used to accurately generate the vehicle bounding box and accurately classify the vehicle category [16]. Machine learning-based algorithms generally use feature operators and classifiers, where the feature operators are manually extracted. This method is generally based on the characteristics of a single scene design.