# A Room Monitoring System Using Deep Learning and Perspective Correction Techniques
## 2 Related Work
### Object Detection  
Object detection is an essential and challenging task in computer vision, and it has been extensively explored for various applications including monitoring and security systems, video surveillance, self-driving cars, and human-computer interaction. Owing to recent advances in deep learning and CNN architectures, CNNs are being widely used in object detection, especially person detection. This has led to the development of many new and high-accuracy object detection methods, such as Faster R-CNN [17], SSD [18], YOLO v3 [19], RetinaNet [20], and Mask R-CNN [21].  
R-CNN based object detection is one of the primary object detectors. [PERSON] et al. [22] proposed the original R-CNN method. They used a selective search algorithm to extract around 2000 region proposals from each input image. Then, these region proposals were utilized to compute features by using a large CNN. Finally, the class-specific linear support vector machines (SVMs) were applied to classify each region. This method that allowed high-capacity CNNs was applied to localize and segment objects. However, it could not be performed in real-time. To solve the drawbacks of the R-CNN method, [PERSON] [23] proposed a Fast R-CNN method to build a faster object detection algorithm. This method improved training and testing speed and increased detection accuracy. [PERSON] et al. [17] presented a Faster R-CNN method for real-time object detection. They introduced Region Proposal Networks (RPNs) to generate efficiently and accurately region proposal. Furthermore, the learned RPN improved region proposal quality and overall object detection accuracy. [PERSON] et al. [21] proposed a MaskR-CNN method for object detection and object instance segmentation in the image. This method extended Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. Moreover, Mask R-CNN is easy to implement and can be trained with a broad range of flexible architecture designs.  
Recently, small object detection has attracted considerable attention. [PERSON] et al. [PERSON] et al. (2019) proposed a small object detection method using selective tile processing. Here, tiles refer to small patches extracted from the input image. They also introduced attention and memory mechanisms for using tiles efficiently. [PERSON] and [PERSON] [PERSON] and [PERSON] (2019) presented an attention pipeline method to detect small objects in high-resolution 4K and 8K videos. This method includes a two-stage evaluation of each image or video frame under rough and refined resolution. The YOLO v2 model was used to detect objects. [PERSON] et al. [PERSON] et al. (2019) proposed an efficient solution for small object detection in high-resolution images. They detected pedestrians and vehicles using a camera onboard a micro aerial vehicle. Additionally, they introduced a tiling-based approach to limit detail loss in object detection. They also showed that their method increases the small object detection accuracy significantly. [PERSON] et al. [PERSON] et al. (2019) demonstrated scale-aware rotated object detection in aerial imagery. They proposed a novel feature fusion module and a new weighted loss to detect small objects in remote sensing images.