# Prototype to Increase Crosswalk Safety by Integrating Computer Vision with ITS-G5 Technologies
## 2 State of Art
### Computer Vision for Pedestrian Detection  
Visual data analysis has been a research topic for more than six decades that provides information, analysis and decision-making. Human visual perception initiates from the identification of reference points of an object which is further identified from the comparative points of a similar object learned by the brain [18]. The research in image and video analysis in most cases will try to mimic these processes of bio-inspired behaviour. In our research of pedestrian detection, we combine computer vision and RGB cameras with ITS-G5 technologies to provide road safety context [19; 20; 21]. In our context, related to pedestrian detection, it is important to implement robust object recognition tasks that accurately give a pose estimation of the object (person), which are the two main tasks related to our computer vision module. The computer vision algorithms extract features from images to perform object detection and recognition, including local and the global features. Local features describe the local vicinity around the selected key points, and in general these algorithms handle scale changes, rotation and occlusion in a more proficient way. As examples, we have Features from Accelerated Segment Test (FAST), the minimum eigenvalue algorithm, the corner detector, Speeded Up Robust Features (SURF), etc. [22; 23]. Global features have an important role as a general descriptor in images. As examples, we have shape distributions, viewpoint feature histogram, geometric scale space, etc. [24; 25]. Recently, new types of algorithms based on artificial intelligence (AI) have emerged in the field of object detection and recognition, image understanding and description. As an example, deep learning-based methods have been used to identify objects. These methods have shown a great success in robust object classification algorithms, but their training procedure requires large amounts of data (labelled dataset images) and a considerable amount of computational demands. Convolutional neural networks (CNN) are the most successful approach in deep learning for interpreting images (two-dimensional data) and video (tree-dimensional data). CNN provides the understanding of semantic context in images and videos and gives state-of-art results in image recognition, detection, segmentation, scene reconstruction, etc. In its typical topology, a CNN consists of three main layers and sub-layers, as follows:  
1. Input layer: receives two-dimensional data (images);
2. Hidden perceptron payers and its sub-layers: * Convolution layer: the core of CNNs that has several learnable filters from data; * Pooling layer: used to reduce the number of parameters and the number of complex computations in training; * Fully connected layer: takes the output of previous layers and turns them into a single vector that can be an input for the next one. 3. Output Layer. The literature [26] presents a technical review to understand the concepts and topologies of different types of deep neural networks. In our work, we use the YOLO software implementation for its accuracy and real time capabilities. The global overview of the YOLO organization blocks are illustrated in Figure 2.  
Figure 2: YOLO main organization blocks, source [27]. In general, computer vision tasks related to object detection and recognition deal with objects under different viewpoints, illumination, intraclass variation, object rotation and scale change, dense and occluded object detection and speed up trade-off of detection. It also has been stated [28] that deep models are potentially more capable than shallow models in handling such complex tasks. There are several scientific works about developments related to our work and that present the major contributes to the state of the art in deep neural networks for pedestrian detection.