## 2 Related Work  
### Conventional Approaches  
Even though most of the current object detection algorithms use deep learning to fulfill their task, earlier methods were heavily dependent on handcrafted features. One such early object detector is the Viola and Jones [1] algorithm, which uses Haar-like functions to represent features in an image. Using a sliding window approach and comparing the pixels in that region to the feature templates is very computationally expensive. Because of this, the authors proposed the usage of a technique called Integral Image, which involves pre-computing a map of the image starting from the top-right corner as follows: the value of each cell \(p^{\prime}_{\ i,\ j}\) is equal to the sum of the values of the cells directly above and to the left as well as the value of the pixel \(p_{i,\ j}\) With this approach, the sum of the pixels in a rectangular area can be quickly computed as \(S_{rect}=p^{\prime}_{b,\ r}+p^{\prime}_{t,\ l}-\left(p^{\prime}_{t,\ r}+p^{\prime} _{b,\ l}\right)\), where \(t=\text{top}\), \(b=\text{bottom}\), \(l=\text{left}\), and \(r=\text{right}\), making it easier to compare with the templates by computing the different sums of the rectangular shapes composing them. Examples of such templates can be seen in Figure 1. The feature selection and matching are performed using AdaBoost (introduced in [PERSON] (2018)), which automatically finds the best Haar-like filters to use from a pool of about 180 k templates. However, using the entire set of templates is computationally expensive. Because of this, the authors proposed the implementation of a cascade of weaker classifiers, each subsequent classifier testing an increasing number of features compared to the previous one. Each window is passed through the cascade network and, if a classifier in the chain considers that no object of interest is present, the algorithm discards the proposal. In this way, the algorithm manages to decrease the inference time per image, as many proposals are discarded in the earlier stages of the cascade network. Figure 2 depicts the flow schematic of the cascade network.  
\"Histogram of Oriented Gradients for Human Detection\", another early approach proposed by [PERSON] and [PERSON] (2018), achieved robustness towards local illumination and small geometric transformations by introducing histogram of oriented gradients (HOG) descriptors, which model an object's shape and appearance by using the gradients of pixel intensities. These are computed regarding a sliding window as follows:  
1. The entire image is color and gamma normalized to be independent of the color space.
2. The detection window is divided into a grid.
3. For each cell in the grid: 1. The gradients are computed in each pixel; 2. A histogram of the gradients in the cell is computed.
4. The cells are grouped into overlapping blocks.
5. Using the individual cell histograms, a histogram is computed for each block.
6. Compute the histogram of gradients over the blocks, using the histograms of the cells.
7. Normalize the block histograms to obtain illumination invariance.
8. Concatenate the block histograms to obtain the window's HOG descriptor.  
The final step is using a classifier over the window's HOG descriptor. In the paper, the authors used a linear support vector machine (SVM) (2018) for this task.  
Figure 1: Examples of the rectangle (Haar-like) features. (**a**) Two-rectangle features. (**b**) Three-rectangle features. (**c**) Four-rectangle features.  
Figure 2: The schematic of the cascade network proposed by [PERSON] and [PERSON] (2018).