| Popis: |
The widespread use of pervasive sensing technolo- gies such as wireless sensors and street cameras allows the de- ployment of crowd estimation solutions in smart cities. However, existing Wi-Fi-based systems do not provide highly accurate crowd size estimation. Furthermore, these systems do not adapt to the dynamic changesin-the-wild, such as unexpected crowd gatherings. This paper presents a new adaptive machine learning system, calledCountMeIn, to address the crowd estimation problem using polynomial regression and neural networks. The approach transfers the calibration task from cameras to machine learning after a short training with people counting from stereo- scopic cameras, Wi-Fi probe packets, and temporal features. After the training, CountMeIncalibrates Wi-Fi using the trained modeland maintains high accuracy for a longer duration without cameras. We test the approach in our pilot study in Gold Coast, Australia, for about five months. CountMeIn achieves 44% and 72% error reductions in minutely and hourly crowd estimations compared to the state-of-the-art methods. |
