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Crowd counting aims to count the number of instantaneous people in a crowded space, and many promising solutions have been proposed for single image crowd counting. With the ubiquitous video capture devices in public safety field, how to…
Device identification is one way to secure a network of IoT devices, whereby devices identified as suspicious can subsequently be isolated from a network. In this study, we present a machine learning-based method, IoTDevID, that recognizes…
We propose a novel crowd counting model that maps a given crowd scene to its density. Crowd analysis is compounded by myriad of factors like inter-occlusion between people due to extreme crowding, high similarity of appearance between…
In recent years, there have been efforts to collect human contact traces during social events (e.g., conferences) using Bluetooth devices (e.g., mobile phones, iMotes). The results of these studies have enabled the ability to do the…
Big data streams are possibly one of the most essential underlying notions. However, data streams are often challenging to handle owing to their rapid pace and limited information lifetime. It is difficult to collect and communicate stream…
Understanding human behaviour in crowded indoor environments is central to surveillance, smart buildings, and human-robot interaction, yet existing datasets rarely capture real-world indoor complexity at scale. We introduce IndoorCrowd, a…
Crowd scene analysis receives growing attention due to its wide applications. Grasping the accurate crowd location (rather than merely crowd count) is important for spatially identifying high-risk regions in congested scenes. In this paper,…
Sequential Recommender Systems (SRSs) aim to predict the next item that users will consume, by modeling the user interests within their item sequences. While most existing SRSs focus on a single type of user behavior, only a few pay…
Collectiveness motions of crowd systems have attracted a great deal of attentions in recently years. In this paper, we try to measure the collectiveness of a crowd system by the proposed node clique learning method. The proposed method is a…
Compressed sensing based multiuser detection (CSMUD) is a promising candidate to cope with the massive connectivity requirements of the massive machine type communication (mMTC) in the fifth generation (5G) wireless communication system. It…
The problem of secure friend discovery on a social network has long been proposed and studied. The requirement is that a pair of nodes can make befriending decisions with minimum information exposed to the other party. In this paper, we…
Community detection in graphs has many important and fundamental applications including in distributed systems, compression, image segmentation, divide-and-conquer graph algorithms such as nested dissection, document and word clustering,…
This paper presents a nonlinear location estimation to infer the position of a user holding a smartphone. We consider a large location with $M$ number of grid points, each grid point is labeled with a unique fingerprint consisting of the…
Performance analysis of queueing networks is one of the most challenging areas of queueing theory. Barring very specialized models such as product-form type queueing networks, there exist very few results which provide provable…
Our research is focused on two main applications of crowd scene analysis crowd counting and anomaly detection In recent years a large number of researches have been presented in the domain of crowd counting We addressed two main challenges…
We consider the problem of tracking a group of mobile nodes with limited available computational and energy resources given noisy RSSI measurements and position estimates from group members. The multilateration solutions are known for…
Radio frequency sensor networks are becoming increasingly popular as an indoor localization and monitoring technology for gaining unobtrusive situational awareness of the surrounding environment. The localization effort in these networks is…
Smartphone sensors based human activity recognition is attracting increasing interests nowadays with the popularization of smartphones. With the high sampling rates of smartphone sensors, it is a highly long-range temporal recognition…
The estimation of crowd count in images has a wide range of applications such as video surveillance, traffic monitoring, public safety and urban planning. Recently, the convolutional neural network (CNN) based approaches have been shown to…
With the rise of hailing services, people are increasingly relying on shared mobility (e.g., Uber, Lyft) drivers to pick up for transportation. However, such drivers and riders have difficulties finding each other in urban areas as GPS…