# ROS-Based Human Detection and Tracking from a Wireless Controlled Mobile Robot Using Kinect
## 5 Experimental Results  
We implemented the ROS-based human detection and tracking system in a laptop equipped with a 2 GHz Intel(r)Core 64-bit i3-5005U CPU and 2 GB system memory running Ubuntu 16.04 operating system. The human detection and tracking result is displayed on the laptop screen through the ROS visualizer. Once the user launches the system, the ROS triggers all the algorithms to start detection and tracking the human. Two runtime scenarios are captured at two different locations in an indoor room to verify the performance of the proposed system. The Kinect device is mounted on the four-wheeled mobile platform to capture images for detecting and tracking humans in real time. Figure 6 shows the configuration of the four-wheeled mobile platform, which is a robotic vehicle chassis having four 12 volts DC motors produced by Nevonsolutions Pvt Ltd [17]. We further install a WI-FI module in this vehicle chassis to help in controlling the mobile platform wirelessly. Moreover, each DC motor is connected to an LM298N motor driver to help in driving the motors in the required direction according to the received control input through the WI-FI module.  
Figure 5: Remote control flowchart of the mobile platform.  
Figure 7 shows the experimental setting used in the experiments. The four-wheeled mobile platform is equipped with a Kinect device and is controlled wirelessly by a cell phone with a control interface. The Kinect mounted on the mobile platform captures live video streams to detect and to track humans, whom can be monitored in the display of the laptop. The mobile platform is controlled through the cell phone via an HTML page for following the detected and tracked human.  
The first runtime scenario is an experiment of interaction between one detected human and the mobile robot through wireless remote control. Figure 8 shows the photos of motion sequences of interaction between the human and the remote-controlled mobile platform during this experiment. Figure 8a,c,e is the input images taken from the Kinect on the mobile platform. The sky-blue colored polygon box in Figure 8b is the human detection result of the proposed system, and the mobile robot started moving forward by the control command given through cell phone by a remote user. Figure 8d,f shows the mobile platform is in motion for tracking the human.  
Figure 6: Configuration of the four-wheeled mobile platform.  
Figure 7: Experimental setting: The four-wheeled mobile platform equipped with a Kinect device and controlled wirelessly by a cell phone with a control interface.  
The second runtime scenario is an experiment of detecting and tracking two humans in the room. Figure 9 shows the image sequence of detecting and tracking two humans using the proposed system  
Figure 8: Recorded image sequence of interaction between one human and the mobile robot through wireless remote control.  
during the second experiment. Figure 9a,c is the input data captured from the Kinect, and Figure 9b,d show that two humans are detected and are tracked individually in two different colored polygons box in a single frame. Therefore, the above experimental results validate the performance of the proposed human detection and tracking system combined with the wireless remote-controlled mobile platform. A video clip of the above experimental results can refer to the website of reference [18].