# A Novel Device-Free Counting Method Based on Channel Status Information
## 1 Introduction  
In some overpopulated countries, the contradiction between the limited indoor space and large population is becoming increasingly prominent. Thus, it is of vital significance to implement crowd counting in public places, e.g., libraries, museums, shopping malls and college classrooms, which are with limited resources and strong mobility. In the meantime, it is a crucial and challenging task to acquire the human traffic or accurately calculate the population in some particular circumstances. Therefore, signal changes caused by human motion has been utilized to acquire crowd counting [1]. Since crowd counting can lead to an efficient utilization of space resources, it has been widely applied to intelligent surveillance, guided to tour, crowd management and urban security, etc. Numerous crowd counting approaches have emerged over the past decades, e.g., video-based recognition, infrared-based induction, and non-image-based localization. However, these methods all require specialized hardware deployment and strict operating conditions which hinder their wide deployment. First, video-based recognition has the advantages of high-accuracy, rapidness and low-cost, and is applicable to various commercial and security fields. However, it is encountered by some constrains, e.g., many blind spots cannot be monitored and the environmental requirements are high [2]. Second, although infrared-based induction has also been widely applied in markets, subways and buses, it will be challenging to obtain a high counting accuracy, when the people flow becomes denser [3]. Third, non-image-based solutions typically require people to carry wearable devices. Unfortunately, it is impractical and expensive to distribute equipments to each individual in a public place, and is not feasible under an emergent event [4, 5]. The legacy crowd counting methods not only consume manpower and materials, but also encounter the statistical error. In recent years, Wireless Sensor Network (WSN) technology has been gradually applied from theoretical research to industries. Besides, some network technologies, e.g., Wi-Fi., are also utilized in the field of crowd counting. The Received Signal Strength Indicator (RSSI) could realize crowd counting from the perspective of localization [6] and estimate human position in small areas or homes [7; 8]. However, the RSSI is only applicable to the MAC layer information at the packet level, and its value will not remain stable with time fluctuation, and it meanwhile, does not provide sufficient recognition and robustness in complex indoor environments, thus the counting error will exist. Nowadays, some commercial ordinary wireless network cards (such as 802.11 a/g/n networks) can provide both amplitude and phase information on different subcarriers, utilizing the form of Channel State Information (CSI) by Orthogonal Frequency Division Multiplexing (OFDM). Different from RSSI, the CSI is a type of physical layer information and could obtain matrix of all subcarriers from transmit antennas to receive antennas [9]. More importantly, the CSI acquired by different subcarriers would exhibit distinct fading. These fading can be suppressd by the Multiple Input Multiple Output (MIMO) technique (such as IEEE 802.11n, 3 GPP LTE, and mobile WiMAX systems), which can increase the capacity, range and reliability of wireless systems from the space dimension [10]. MIMO could enhance data throughput and transmission distance, without additional bandwidth and transmission power. Thus, CSI has both frequency diversity provided by OFDM and spatial diversity supplied by MIMO. As the specific RSSI at the physical layer, CSI is expected to enable more accurate and reliable detections, which has also attracted intensive attentions from both academic and industrial domains, and thus it would be a promising one in crowd counting. The contradiction between limited indoor space and large population necessitates crowd number counting [1].