## 5 Overview of Traditional and DL Approaches for Object Detection in AVs
### Traditional Approach  
Various algorithms were developed for the purpose of OD. Prominent traditional feature extraction methods include Scale Invariant Feature Transform (SIFT) [83], Viola-Jones rectangles [84], Haar-Like-wavelets and Histogram of Oriented Gradient (HOG) [85], Adaptive Boosting (AdaBoost), non-linear Support Vector Machines (SVMs), and linear SVMs, which are common classical approaches for object classification. A number of steps are involved in the SIFT algorithm [83], which is well known for its ability to determine scale and rotation-invariant features, making it resistant to partial occlusion, clutter, noise, and lighting variations. These include the detection of scale-space extrema, the localization of key points, the assignment of orientations, the creation of keypoint descriptors, and the final step of using key point matching to recognize objects. Descriptors are used to compare and identify items in a database using methods like nearest neighbor indexing and the Hough transform. Nevertheless, SIFT has some drawbacks, including the absence of ongoing key point consistency in dynamic objects, the significant dimensions of feature descriptors that can affect matchmaking, and some restrictions related to patents. Figure 8: Performance graph of traditional and DL approaches. Figure 7: The basic framework for object detection systems. The Viola-Jones approach (Viola and Jones, [84] was developed for human face identification using Haar-like feature extractors. It consists of four primary parts; the primary image algorithm for effective image participation is AdaBoost. Haar-like feature extraction is used to uncover biased features. Cascade classifier implementation is used to filter out background data and concentrate on regions that are more likely to contain the object of interest. The algorithm performs remarkably well, taking only 0.067 s to process a \(384\times 288\) pixel image. Its detection rate is 91.4%, and it produces 50 false positives. Nevertheless, it is not well adapted for the generic recognition of objects and has slow periods for training. The HOG method was created for identifying humans in digital photos and was first presented in [85]. In order to determine the gradient's length and direction, this method examines input images employing gradient filters, both horizontal and vertical. The filtered images are divided into \(8\times 8\) pixel cells, which are then further divided into \(2\times 2\) cell blocks that have 50% overlap. For every cell, orientation histograms are produced, quantizing gradient directions into nine bins ranging from 0\({}^{\circ}\) to 180\({}^{\circ}\). The magnitudes of these bins are used as votes. A vector representing the HOG is produced by concatenating these histograms for every cell in a block. To take into consideration changes in illumination and contrast, these vectors are standardized. The final HOG descriptor, which consists of all the normalized blocks, is fed into a Support Vector Machine (SVM) to determine if an image is human or not. Although the HOG algorithm's dense grid method results in a larger processing burden and less efficient performance in cases involving obscured objects, it is effective in decreasing false positives when compared with Haar wavelets. These AI techniques, which have their roots in statistical principles, have traditionally formed the basis of machine learning models. These techniques enable computers to recognize trends and forecast outcomes based on past data. However, even with their historical importance, traditional methods have clear drawbacks. Reliance on manual feature engineering, as shown in Figure 9, in which experts carefully craft features for the model necessitates domain knowledge and frequently fails to capture the nuances of intricate datasets.