Unmanned aerial vehicles(UAVs)have recently attractedwidespread attention in civil and commercial applications.For example,UAVs(or drone)technology is increasingly used in crowd monitoring solutions due to its wider a...Unmanned aerial vehicles(UAVs)have recently attractedwidespread attention in civil and commercial applications.For example,UAVs(or drone)technology is increasingly used in crowd monitoring solutions due to its wider air footprint and the ability to capture data in real time.However,due to the open atmosphere,drones can easily be lost or captured by attackers when reporting information to the crowd management center.In addition,the attackers may initiate malicious detection to disrupt the crowd-sensing communication network.Therefore,security and privacy are one of the most significant challenges faced by drones or the Internet of Drones(IoD)that supports the Internet of Things(IoT).In the literature,we can find some authenticated key agreement(AKA)schemes to protect access control between entities involved in the IoD environment.However,the AKA scheme involves many vulnerabilities in terms of security and privacy.In this paper,we propose an enhancedAKAsolution for crowdmonitoring applications that require secure communication between drones and controlling entities.Our scheme supports key security features,including anti-forgery attacks,and confirms user privacy.The security characteristics of our scheme are analyzed byNS2 simulation and verified by a random oracle model.Our simulation results and proofs show that the proposed scheme sufficiently guarantees the security of crowd-aware communication.展开更多
Real time crowd anomaly detection and analyses has become an active and challenging area of research in computer vision since the last decade.The emerging need of crowd management and crowd monitoring for public safet...Real time crowd anomaly detection and analyses has become an active and challenging area of research in computer vision since the last decade.The emerging need of crowd management and crowd monitoring for public safety has widen the countless paths of deep learning methodologies and architectures.Although,researchers have developed many sophisticated algorithms but still it is a challenging and tedious task to manage and monitor crowd in real time.The proposed research work focuses on detection of local and global anomaly detection of crowd.Fusion of spatial-temporal features assist in differentiation of feature trained using Mask R-CNN with Resnet101 as a backbone architecture for feature extraction.The data from,BIWI Walking Pedestrian dataset and the Crowds-By-Examples(CBE)dataset and Self-Generated dataset has been used for experimentation.The data deals with different situations like one set of data deals with normal situations like people walking and acting individually,in a group or in a dense crowd.The other set of data contains images four unique anomalies like fight,accident,explosion and people behaving normally.The simulated results show that in terms of precision and recall,our system performs well with Self-Generated dataset.Moreover,our system uses an early stopping mechanism,which allows our system to outperform to make our model efficient.That is why,on 89th epoch our system starts generating finest results.展开更多
基金This work was supported by the Deputyship for Research&Innovation,Ministry of Education(in Saudi Arabia)through the Project Number(227).
文摘Unmanned aerial vehicles(UAVs)have recently attractedwidespread attention in civil and commercial applications.For example,UAVs(or drone)technology is increasingly used in crowd monitoring solutions due to its wider air footprint and the ability to capture data in real time.However,due to the open atmosphere,drones can easily be lost or captured by attackers when reporting information to the crowd management center.In addition,the attackers may initiate malicious detection to disrupt the crowd-sensing communication network.Therefore,security and privacy are one of the most significant challenges faced by drones or the Internet of Drones(IoD)that supports the Internet of Things(IoT).In the literature,we can find some authenticated key agreement(AKA)schemes to protect access control between entities involved in the IoD environment.However,the AKA scheme involves many vulnerabilities in terms of security and privacy.In this paper,we propose an enhancedAKAsolution for crowdmonitoring applications that require secure communication between drones and controlling entities.Our scheme supports key security features,including anti-forgery attacks,and confirms user privacy.The security characteristics of our scheme are analyzed byNS2 simulation and verified by a random oracle model.Our simulation results and proofs show that the proposed scheme sufficiently guarantees the security of crowd-aware communication.
文摘Real time crowd anomaly detection and analyses has become an active and challenging area of research in computer vision since the last decade.The emerging need of crowd management and crowd monitoring for public safety has widen the countless paths of deep learning methodologies and architectures.Although,researchers have developed many sophisticated algorithms but still it is a challenging and tedious task to manage and monitor crowd in real time.The proposed research work focuses on detection of local and global anomaly detection of crowd.Fusion of spatial-temporal features assist in differentiation of feature trained using Mask R-CNN with Resnet101 as a backbone architecture for feature extraction.The data from,BIWI Walking Pedestrian dataset and the Crowds-By-Examples(CBE)dataset and Self-Generated dataset has been used for experimentation.The data deals with different situations like one set of data deals with normal situations like people walking and acting individually,in a group or in a dense crowd.The other set of data contains images four unique anomalies like fight,accident,explosion and people behaving normally.The simulated results show that in terms of precision and recall,our system performs well with Self-Generated dataset.Moreover,our system uses an early stopping mechanism,which allows our system to outperform to make our model efficient.That is why,on 89th epoch our system starts generating finest results.
