Over 500,000 Car Tracking Devices' Passwords Accidentally Leaked As a Consequence of Misconfigured Cloud Server > 자유게시판

In one more case of an unintentional information leak, login credentials of over 500,000 automotive monitoring gadgets were freely uncovered attributable to a misconfigured cloud server. SVR allows its prospects to track their vehicles spherical the clock, to allow them to monitor and recuperate them in case their automobile has been stolen. The agency attaches a tracking device to a car in a discreet location, so if the automobile is stolen, an unknown driver would have no information of it being monitored. In response to researchers at Kromtech Security, who found the breach, the data exposed included SVR users' account credentials, akin to emails and passwords. Users' automobile information, together with VIN numbers and licence plates were additionally freely uncovered. The data was exposed via an insecure Amazon S3 bucket. Kromtech researcher Bob Diachenko said in a weblog. SVR's car tracking device screens in every single place a vehicle has been for iTagPro bluetooth tracker the past one hundred twenty days, which will be simply accessed by anybody who has access to users' login credentials. The insecure Amazon S3 bucket has been secured, after Kromtech reached out to SVR and notified them concerning the breach. It nonetheless remains unclear as to how long the information remained freely uncovered. It is usually unsure whether or not the data was probably accessed by hackers.

Legal standing (The authorized status is an assumption and is not a authorized conclusion. Current Assignee (The listed assignees could also be inaccurate. Priority date (The precedence date is an assumption and isn't a authorized conclusion. The appliance discloses a goal tracking technique, a target tracking device and electronic equipment, and relates to the technical field of artificial intelligence. The tactic includes the following steps: a first sub-community in the joint tracking detection network, a first feature map extracted from the target feature map, and a second function map extracted from the target feature map by a second sub-community within the joint tracking detection network; fusing the second characteristic map extracted by the second sub-network to the primary characteristic map to acquire a fused characteristic map corresponding to the primary sub-network; acquiring first prediction data output by a first sub-network primarily based on a fusion feature map, and buying second prediction information output by a second sub-network; and determining the present position and the motion path of the shifting goal in the target video based on the first prediction data and the second prediction info.

The relevance among all of the sub-networks which are parallel to one another might be enhanced via characteristic fusion, and the accuracy of the determined position and movement path of the operation goal is improved. The current application relates to the sphere of artificial intelligence, and in particular, to a target tracking method, apparatus, and digital machine. In recent times, artificial intelligence (Artificial Intelligence, AI) technology has been extensively used in the sector of goal monitoring detection. In some scenarios, a deep neural network is typically employed to implement a joint trace detection (monitoring and object detection) community, where a joint trace detection network refers to a community that's used to achieve goal detection and goal trace collectively. In the existing joint monitoring detection network, the position and motion path accuracy of the predicted transferring target will not be high sufficient. The applying provides a target tracking method, a target tracking device and digital gear, iTagPro bluetooth tracker which can improve the problems.

In one facet, an embodiment of the current software offers a goal monitoring methodology, the place the method consists of: a first sub-network in a joint monitoring detection network is used for extracting a primary characteristic picture from a goal characteristic picture, and a second sub-network within the joint monitoring detection network is used for extracting a second characteristic image from the goal characteristic picture, whereby the target characteristic picture is extracted from a video body of a target video; fusing the second characteristic map extracted by the second sub-community to the first feature map to obtain a fused function map corresponding to the primary sub-network; buying first prediction info output by a first sub-community in response to the fusion function map, and acquiring second prediction info output by a second sub-network; based mostly on the primary prediction information and the second prediction data, determining the present place and the motion trail of the moving target within the target video.