# An Adaptive Ensemble Approach to Ambient Intelligence Assisted People Search
## 5 Real-World Experiments  
In our experimental environment, three kinds of smart devices were deployed, as shown in Figure 5:  
1. Nodes: Both Bluetooth low-energy (BLE) and Ethernet-enabled gateway devices were mounted on the walls at each corners of the room. We used \"Raspberry Pi3\" as WSN nodes in the experiment. They continuously monitored other BLE devices activities and communicated with the robot wirelessly. Each WSN node maintained a lightweight classifier to recognize nearby people within its communication range. 2. iTag: These are battery powered BLE enabled tags. We used \"OLP425\" in the experiment, which is a BLE chip produced by U-blox. The OLP425 tags are small in size, with limited memory and a short communication range of up to 20 m. They support ultra-low power consumption, and they are suitable for applications using coin cell batteries. Each iTag saves personal information such as a name and a face photo in their non-volatile memory, and it accepts enquiries from the WSN node. 3. Robot: The robot is a Pioneer 2-DX8-based two-wheel-drive mobile robot and it contains all of the basic components for sensing and navigation in a real-world environment, including battery power, two drive motors, and a free wheel, and position/speed encoders. The robot was customized with an aluminum supporting pod to mount a pan/tilt camera for taking snapshots of people's faces for the recognition tasks. A DXE4500 fanless PC with Intel core i7 and 4 GB RAM on board with wireless communication capability handles all the processing tasks, including controlling and talking to the WSN nodes. The model of the camera on the robot is \"DCS-5222L\", which supports a high-speed 802.11 n wireless connection. The snapshot resolution is fixed at 1280 \(\times\) 720 pixels. When people were nearer to the camera (within 4 m), the face area was clear and occupied a large number of pixels (240 \(\times\) 240) in \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Overlapped Data** & **Photo Size** & **240 \(\times\) 240** & **120 \(\times\) 120** & **90 \(\times\) 90** & **60 \(\times\) 60** & **30 \(\times\) 30** & **20 \(\times\) 20** & **Average** \\ \hline \multirow{3}{*}{35\%} & Local SVM & 13.76\% & 14.07\% & 12.72\% & 14.74\% & 13.62\% & 15.93\% & 14.14\% \\  & Our method & 10.58\% & 11.11\% & 11.38\% & 12.70\% & 13.76\% & 15.87\% & 12.57\% \\ \hline \multirow{3}{*}{50\%} & Local SVM & 13.49\% & 13.99\% & 12.41\% & 13.76\% & 13.60\% & 16.72\% & 13.99\% \\  & Our method & 9.27\% & 10.56\% & 10.43\% & 12.21\% & 11.73\% & 15.16\% & 11.56\% \\ \hline \multirow{3}{*}{75\%} & Local SVM & 13.23\% & 13.78\% & 12.33\% & 13.70\% & 13.12\% & 16.90\% & 13.84\% \\  & Our method & 10.85\% & 11.11\% & 10.85\% & 12.43\% & 12.96\% & 15.87\% & 12.35\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of false positive rates with local classifiers. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Overlapped Data** & **Photo Size** & **240 \(\times\) 240** & **120 \(\times\) 120** & **90 \(\times\) 90** & **60 \(\times\) 60** & **30 \(\times\) 30** & **20 \(\times\) 20** & **Average** \\ \hline \multirow{3}{*}{35\%} & local SVM & 86.64\% & 86.77\% & 87.63\% & 86.26\% & 84.02\% & 78.24\% & 84.93\% \\  & Our method & 91.27\% & 90.34\% & 90.34\% & 89.02\% & 87.70\% & 83.86\% & 88.76\% \\ \hline \multirow{3}{*}{50\%} & Local SVM & 86.90\% & 86.84\% & 88.07\% & 86.80\% & 84.05\% & 78.77\% & 85.24\% \\  & Our method & 93.21\% & 91.38\% & 91.44\% & 90.22\% & 88.96\% & 83.98\% & 89.87\% \\ \hline \multirow{3}{*}{75\%} & Local SVM & 87.30\% & 87.51\% & 88.25\% & 86.93\% & 84.26\% & 79.18\% & 85.57\% \\  & Our method & 92.59\% & 89.95\% & 90.61\% & 88.49\% & 88.23\% & 82.67\% & 88.76\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of recognition rates with local classifiers. the camera view. When people were further from the camera (around 10 m away), the face region in the camera view became smaller and blurrier, with approximately 30 \(\times\) 30 pixels.