Towards In-baggage Suspicious Object Detection Using Commodity WiFi

Abstract

The growing needs of public safety urgently require scalable and low-cost techniques on detecting dangerous objects (e.g., lethal weapons, homemade-bombs, explosive chemicals) hidden in baggage. Traditional baggage check involves either high manpower for manual examinations or expensive and specialized instruments, such as X-ray and CT. As such, many public places (i.e., museums and schools) that lack of strict security check are exposed to high risk. In this work, we propose to utilize the finegrained channel state information (CSI) from off-the-shelf WiFi to detect suspicious objects that are suspected to be dangerous (i.e., defined as any metal and liquid object) without penetrating into the user's privacy through physically opening the baggage. Our suspicious object detection system significantly reduces the deployment cost and is easy to set up in public venues. Towards this end, our system is realized by two major components: it first detects the existence of suspicious objects and identifies the dangerous material type based on the reconstructed CSI complex value (including both amplitude and phase information); it then determines the risk level of the object by examining the object's dimension (i.e., liquid volume and metal object's shape) based on the reconstructed CSI complex of the signals reflected by the object. Extensive experiments are conducted with 15 metal and liquid objects and 6 types of bags in a 6-month period. The results show that our system can detect over 95% suspicious objects in different types of bags and successfully identify 90% dangerous material types. In addition, our system can achieve the average errors of 16ml and 0.5cm when estimating the volume of liquid and shape (i.e., width and height) of metal objects, respectively.

Clutter removal for Wi-Fi-based passive bistatic radar

“…To separate targets from the noise floor on the RDM, a threshold is applied on each cell of the RDM. The most common technique is CA-CFAR, for which the threshold is based on the noise power estimateσ 2 n on a band of size D centered on the RDM cell. Here the band is 1-dimensional along the range dimension of the RDM [10].…”

“…to remotely monitor movements of people or objects in buildings. Examples include control of building evacuations and intrusion detection [1], assistance of security staffs in airports or public places [2], or human movements classification [3]. Indoor monitoring with radar is of interest when cameras do not have line-of-sight or cause privacy concerns.…”

Wi-Fi CSI-Based Outdoor Human Flow Prediction Using a Support Vector Machine

“…In the case of non-moving objects, the multipath propagation is different depending on the location or the shape of objects where they exist between antennas. This characteristic is used for location estimation and object identification [6][7][8][9][10] among others. In these applications, the main classification algorithms for estimation, such as SVMs and convolutional neural networks (CNNs), are used.…”

“…The CSI consists of frequency and space information, expresses the effect of multipath propagation, and corresponds to the space state between transmitting and receiving antennas. To date, CSI has been used for various studies such as human movement detection, human behavior classification, location estimation, object identification, human counting in a room, and human counting in a passageway [3,[5][6][7][8][9][10][11][12][13][14][15][16][17][18][19].…”

Sensing Our World Using Wireless Signals

“…TagScan 10 identifies the target material by detecting the changes of the RSSI and phase when the RFID signal penetrates through the target and it is sensitive enough to differentiate between Coke and Pepsi. Wang et al 19 explores the feasibility of using CSI from COTS WiFi to detect suspicious objects (i.e., liquid objects and metal) hidden in baggage. LiquID 11 employs the UWB signals to identify liquids at high accuracies.…”