CrowdTracking: Real-Time Vehicle Tracking Through Mobile Crowdsensing

Chen, Huihui; Guo, Bin; Yu, Zhiwen; Han, Qi

doi:10.1109/jiot.2019.2901093

Cited by 47 publications

(10 citation statements)

References 30 publications

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“…CrowdTracker [11] is a target tracking system based on mobile crowdsensing, which recruits people to collaboratively take photos of the target. CrowdTracking [7] can rapidly locate a vehicle by using the photographing contexts and the road network, then estimate the vehicle's speed according to two successive localization results.…”

Section: B Target Trackingmentioning

confidence: 99%

“…Once a hit-and-run or child abduction happens, the police usually searches the suspect from records of the pre-deployed video surveillance system [1][2][3] and reports from casual witnesses as in AMBER Alert1. Since the coverage area of video surveillance system is limited, and the coverage by casual witnesses are not stable, a natural idea is building a more available tracking system that contains not only the stationary cameras, but also the mobile phones carried by ordinary citizens [7]. We call this idea as "collaborative sensing".…”

Section: Introductionmentioning

confidence: 99%

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Co-Tracking: Target Tracking via Collaborative Sensing of Stationary Cameras and Mobile Phones

Han

et al. 2020

IEEE Access

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Tracking moving objects in a city, such as suspicious vehicles or persons, is important for public safety management. Traditionally, target tracking is assisted by the pre-deployed stationary surveillance cameras, which are with insufficient coverage. In this work, we propose a different approach called Co-Tracking, a real-time target tracking system that leverages both citizens' mobile phones and stationary surveillance cameras to track moving objects collaboratively. Two key techniques are focused. Firstly, in order to accurately assign tracking tasks, we propose the Middle Query Location Prediction (MQLP) algorithm for predicting the target's location. Secondly, in order to efficiently utilizes these human/machine resources, we propose a heuristic algorithm, namely S-Maximum, to optimize the task allocation, including maximizing the number of completed tracking tasks and minimizing the number of mobile phones. Experimental results show that the proposed Co-Tracking system can effectively track moving objects with low incentive costs.

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Section: B Target Trackingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Co-Tracking: Target Tracking via Collaborative Sensing of Stationary Cameras and Mobile Phones

Han

et al. 2020

IEEE Access

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“…These topics have encouraged researchers in different specialties to contribute and to improve the estimation of a crowded area via various methods and related tasks such as density estimation [9] counting [9], [10,11], tracking [11], behavior analysis [12]. All these tasks can be extracted from a crowded scene and there can be applied for different applications.…”

Section: ) Disaster Managementmentioning

confidence: 99%

Open Challenges for Crowd Density Estimation

Alshaya¹

2020

IJACSA

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Nowadays, many emergency systems and surveillance systems are related to the management of the crowd. The supervision of a crowded area presents a great challenge especially when the size of the crowd is unknown. This issue presents a point of start to the field of the estimation of the crowd based on density or counts. The density of a crowded area is one of the important topics dealt with in many kinds of applications like surveillance, security, biology, traffic. In this paper, we try not only to present a deep review of the different approaches/techniques used in the previous works to estimate the size of the crowd but also to describe the different datasets used. A comparison of some related works based on the weakness and the strength features of each approach is highlighted to show the important research key related to the field of the estimation of the crowded area.

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“…These attackers can be deployed on connected vehicles, which are already part of the system, making them more difficult to detect than static attackers. Unlike tracking using cameras [11], passive mobile attackers rely on communication to track vehicles and can employ a range of strategies to avoid being visually detected. However, these strategies are not yet defined.…”

Section: Introductionmentioning

confidence: 99%

Federated learning-based scheme for detecting passive mobile attackers in 5G vehicular edge computing

Boualouache

Engel

2021

Ann. Telecommun.

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Detecting passive attacks is always considered difficult in vehicular networks. Passive attackers can eavesdrop on the wireless medium to collect beacons. These beacons can be exploited to track the positions of vehicles not only to violate their location privacy but also for criminal purposes. In this paper, we propose a novel federated learning-based scheme for detecting passive mobile attackers in 5G Vehicular Edge Computing. We first identify a set of strategies that can be used by attackers to efficiently track vehicles without being visually detected. We then build an efficient Machine Learning (ML) model to detect tracking attacks based only on the receiving beacons. Our scheme enables Federated Learning (FL) at the edge to ensure collaborative learning while preserving the privacy of vehicles. Moreover, FL clients use a semi-supervised learning approach to ensure accurate self-labeling. Our experiments demonstrate the effectiveness of our proposed scheme to detect passive mobile attackers quickly and with high accuracy. Indeed, only 20 received beacons are required to achieve 95% accuracy. This accuracy can be achieved within 60 FL rounds using 5 FL clients in each FL round. The obtained results are also validated through simulations.

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CrowdTracking: Real-Time Vehicle Tracking Through Mobile Crowdsensing

Cited by 47 publications

References 30 publications

Co-Tracking: Target Tracking via Collaborative Sensing of Stationary Cameras and Mobile Phones

Co-Tracking: Target Tracking via Collaborative Sensing of Stationary Cameras and Mobile Phones

Open Challenges for Crowd Density Estimation

Federated learning-based scheme for detecting passive mobile attackers in 5G vehicular edge computing

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