# A Systematic Deep Learning Based Overhead Tracking and Counting System Using RGB-D Remote Cameras
## 2 Related Work
### Head Tracking and Counting using RGB-D Datasets  
From the past few years, due to the arrival of depth cameras and sensors, many researchers are carrying out studies on head tracking and counting using depth imagery. Table 2 gives a detailed account of these recent systems.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Paper Name** & **RGB-D Datasets** & **Methodologies** & **Classification/Regression Results** \\ \hline People counting base & & The Nearest Neighbor interpolation technique was used for background subtraction which results in greater computational effeciency. Candidate heads and shoulders were detected using edge detection and circle detection techniques. The tracking of heads was done by tracking the circle using the nearest neighbor interpolation method. Finally, a virtual line is used to count the people crossing that line. & The precision results obtained were based on two scenerios, (1) Two to five people were passing the line without holding any object and, (2) People with bags, luggage and crossing the virtual line. The precision results in both scenerios were 100\% and 95\%, respectively. & \\ Benchmark Data and & & The system was developed to calculate the number of people entering or exiting a bus. & \begin{tabular}{c} The dataset is divided into four parts that are identified by \\ \(N^{-}\)\(C^{-}\),\(N^{-}\)\(C^{+}\),\(N^{+}\)\(C^{-}\),\(N^{+}\)\(C^{+}\). \\ People counting accuracy over these four parts on entering were 85.40\%, 83.25\%, 77.54\% and 75.32\%. People counting accuracy rate over these four parts upon exiting were 93.04\%, 92.66\%, 93.71\% and 91.30\%, respectively. \\ \end{tabular} \\ \hline Real-time people counting from depth imagery of crowded environments [42] & MICC dataset & This system was developed to count people in crowded environments. In order to detect the head of an individual, background subtraction was implemented first using selective running average background subtraction. For head tracking, connected components are used. To eliminate the problem of overlapping \\ environments [42] & & CBSR dataset \\ \hline \begin{tabular}{c} Depth driven people \\ counting using deep \\ region proposal \\ network [43] \\ \end{tabular} & CBSR dataset & The main goal of this system was to count people in crowded environments. The authors used CNN for head detetcion. The authors also explored the impact of the number and quality of RPN anchors over the faster RCNN model and improved the performance by proposing a new solution. \\ \hline \begin{tabular}{c} 3D Head Pose \\ Estimation through \\ Facial Features and \\ Deep Convolutional \\ Neural Networks [44] \\ \end{tabular} & \
\begin{tabular}{c} Pointing’04, BU, \\ AFLW, and \\ ICT-3 DHPE \\ \end{tabular} &
\begin{tabular}{c} The authors introduced an end to end face parsing algorithm which tries to address a challenging problem of face pose estimation. The face parsing model through DCNNs by extracting useful information from different face parts was trained. The face parsing model provides a class label for each pixel in a face image. We use a probabilistic classification technique and create PMAPS in the form of grey scale images for each face class  
\end{table}
Table 2: Detailed explanation of head tracking and counting systems that used RGB-D datasets.