Crowd Counting Researches
I’m not a research but I like reading papers to know and learn about new technologies and trends because many papers later on are useful in the future. For instance, I read about Notos, which is a dynamic reputation system; Pleiades, which is a DGA malware detection system, and Phoenix, which is another DGA botnets searching system. These papers helped me to understand how Domain Generation Algorithms work and, therefore, helped me to win the ISACA Challenge in 2015. Today, I want to write about some papers I’ve been reading about Crowd Counting.
I knew a little bit about crowd counting using computer-vision techniques which is useful to know how many people are in the area. However, after reading more about crowd counting, I’ve realised that crowd counting is interesting for many other applications. For instance, crowd counting can be used in smart buildings to optimize the energy consumption based on the number of people in the building or crowd counting can also be used by retailers for better plan their business by assessing which parts of the store get more visitors. In addition, crowd counting is not only investigated from computer-vision but also from environmental science communities and wireless networking.
I didn’t know environmental science communities also studied about crowd counting. They utilize the characteristics of the area of interest such as temperature, concentration of carbon dioxide, lighting, relative humidity, motion, acoustics, etc to identify the number of people in the area. It’s interesting. However, it requires installing specialized sensors such as gas detection sensors, ambient sensors or CO2 sensors, which are expensive. In addition, it requires access to the area of interest.
Wireless networking is another technique to identify the number of people in the area where radio frequency (RF) signals can penetrate through objects, such as walls, that combined with wireless devices, such as WiFi routers, provide a great potential for imaging, tracking, and occupancy estimation. There are two methods using RF signals, which are the device-based active methods and the device-free passive methods.
The device-based active methods rely on pedestrians to carry smartphones. For instance, device-based active methods can use GPS or Bluetooth to assess crowd density. It’s interesting how some researches are based in the walking speed of pedestrians to know the crowd density. However, the device-free passive methods don’t require people to carry any device. Instead, device-free methods rely on the interaction of the wireless signals with the people in the area of interest. It’s interesting how these methods can count people through walls using WiFi.
|Crowd Counting Through Walls Using WiFi|
Once I read the first paper “Crowd Counting Through Walls Using WiFi”, I wanted to know and learn more and more. After reading this paper, I also read “Occupancy Detection Through An Extensive Environmental Sensor Network In An Open-Plan Office Building”, “Indoor Occupancy Estimation From Carbon Dioxide Concentration”, “Bluetooth Based Collaborative Crowd Density Estimation with Mobile Phones” and “Probing Crowd Density Through Smartphones In City-Scale Mass Gatherings”. It’s been interesting, it’s been funny reading about Crowd Counting Researches.
Regards my friends. Keep studying. Keep reading!