ones tend to be rather slow. They sacrifice accuracy for detection frequency by skipping the region proposal stage and detecting the objects directly on the feature maps. Because of this, the predictions of the network tend to be dense, and this can cause the network to misclassify an object or omit it altogether. More recent research on dense object detectors (single-stage detectors) managed to improve their accuracy while maintaining a decent detection frequency, closing the gap with two-stage detectors.  
Other than the aforementioned methods, there have been many different approaches and researches conducted in recent years in the domain of object detection, amongst which are salient-based methods, such as the one in [27]. These are not of primary interest to our paper, but they have been analyzed and described in other literature reviews, such as the ones in [28; 29; 30; 31; 32; 33].  
The focus of this paper is to create an algorithm that uses both object detection paradigms and single-stage and two-stage object detectors. Our method aims to combine both these approaches in order to obtain higher accuracy when compared to other, similar methods at similar inference times. To achieve this, starting from the implementation of YOLOv4 [14], a regression and classification head is added, along with a region of interest pooling (ROI) layer, like that of Faster R-CNN [4]. The high-confidence candidates that do not overlap (i.e., IoU \(<\) threshold) with those outputted by YOLOv4 are then sent as input to the ROI pooling layer, which aligns the bounding boxes with the feature maps, each proposal being then regressed and classified. Finally, the concatenation of the proposals of both the regression and classification head and YOLOv4 is filtered by using non-maximum suppression. This method reduces the number of false-positive detections, as only the best proposals of either YOLOv4 or the regression and classification head are kept after filtering. Another benefit of this method is that, due to the regression and classification head, this model can be used in a \"people tracking by detection\" framework, acting both as a detector, as well as regressing the position of people in the current frame based on their position in the previous frame (see [PERSON] et al. [34]).  
This article is divided into seven sections that are arranged as follows: The Section 1 is the Introduction, which provides details regarding the importance of people detection in RGB images as well as our proposed method and contributions. The Section 2 is Related Work, which describes some of the more common approaches in the field, as well as presenting some of the more recent approaches in the past years. The Section 3 is Used Datasets and Training Tricks and details some approaches one could take when training their CNNs to improve the results, as well as the datasets we considered for our research. The Section 4 is Materials and Methods, which describes the architecture we implemented as well as some of the steps we took to ensure our model was properly trained. The Section 5 is Results, in which we present the results we obtained with our datasets as well as some of the metrics involved, such as precision, recall, and mean-average precision (mAP). The Section 6 is Discussions, which analyzes the results we obtained as well as presenting the main benefits and detriments of our approach when compared to other state-of-the-art approaches. The Section 7 is Conclusions and Future Directions, which summarizes what was achieved in this paper, as well as presenting some of the future work that could be performed regarding improving our method.