# A Boosted 3-D PCA Algorithm Based on Efficient Analysis Method
## 1 Introduction  
The rapid advancement of information and communications technology (ICT) has led to the active commercialization of the autonomous vehicle industry. Research into technologies that can detect the traffic environment is essential for establishing an autonomous driving infrastructure. One of the technologies for establishing infrastructure, the charge coupled device (CCD) camera, is limited in detecting objects in a complex traffic environment. In order to overcome this limitation, camera-based detection systems use various types of image detection technologies [1, 2, 3]. Image detection technology has trouble obtaining high accuracy performance due to environmental factors such as changes in the luminescence of the light that strikes an object, the size of the object, and the moving direction of the object. Recently, a convolutional neural network (CNN) [4] structure has been used to solve these problems. The CNN structure helps obtain a pattern to detect an image. It can detect an object with high performance by self-correcting the object region through the pattern data. However, it can require a vast amount of input data to obtain high detection accuracy performance. Furthermore, as the CNN model is like a black-box, there is no method to verify the correct detection process. Accordingly, CNN-based image detection cannot be easy to trust the judgment of autonomous vehicles controlled because it cannot be confirmed to trust a basis to calculate an estimated value for the detection result. The main cause for this problem is that the CNN structure consists of complex functions with many non-linear elements to calculate the detection result [5, 6, 7, 8, 9, 10, 11, 12, 13, 14]. Therefore, a detection system that can reliably determine the judgment of an autonomous vehicle by analyzing the characteristics of an object along with high-precision result inference may be required. The image detection techniques that require analysis depend on different types of linear element techniques. It is used in three types depending on the method: holistic,feature-based, and hybrid. In the holistic object recognition method, the input of the object recognition system uses the entire object area. In general, the holistic object recognition method has the advantage of being implemented. However, it has been a disadvantage that leads to lower detection accuracy due to the high complexity of object recognition, such as overlapping, distortion, and deformation. The feature-based method detects spatial features such as the eyes, nose, and mouth extracted, and then the locations. Although it shows a method better than the holistic method, optimally extracting features is complicated by the large volume of spatial feature information. Finally, the hybrid method has the advantages of each technology as it is a mix of holistic and feature-based methods. This paper will design how object detection can be improved in general images using a detection method based on the PCA [15] structure. Spatial feature extraction using PCA is a multiple variable analysis method and a usual dimensional reduction method. However, PCA-based detection methods obtain heavier covariance matrices by vectoring two-dimensional images in one dimension. To overcome this problem, we use methods to obtain effective computational complexity. One method uses the 2-D PCA (two-dimensional PCA) algorithm [16], which inputs the 2D features of the image as it is without changing the column vector of the image. However, 2-D PCA has the advantage of efficiently extracting the properties of objects, but it has been a disadvantage to solve that it is difficult to extract location features. Further, the more data, the more difficult it is to extract location features, the lower the detection performance. In order to solve these problems, a PCA-based feature extraction method is required considering object features and location features.