Interesting applications of crowdsensing include measurement of crowdedness at public places and evaluating the extent of social interactions between people, at large gatherings. These require enabling the accurate estimation of proximity between two or more people. Since mobile phones have emerged as the most ubiquitous sensing and computing platform, carried by almost all people close to their body, it is logical to use the same for proximity detection. Further, in order to motivate people to use such application, it is necessary to estimate distances accurately, using only short blocks of sampled signal strengths. In this paper the authors present a mobile based proximity detection system, codenamed BlueEye which is based on Bluetooth. To achieve better distance estimates, BlueEye proposes a new form of path loss model which takes into account the relative orientation of mobile phones. The results show enhanced distance estimates when the separation between devices is less than 8 feet.Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored.Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org