Accurate　occupancy　information　of　indoor　environments　is　one　of　the　key　prerequisites　for　many　pervasive　and　context-aware　services,　e.g.　smart　building/home　systems.　Some　of　the　existing　occupancy　inference　systems　can　achieve　impressive　accuracy,　but　they　either　require　labour-intensive　calibration　phases,　or　need　to　install　bespoke　hardware　such　as　CCTV　cameras,　which　are　privacy-intrusive　by　default.　In　this　paper,　we　present　the　design　and　implementation　of　a　practical　end-to-end　occupancy　inference　system,　which　requires　minimum　user　effort,　and　is　able　to　infer　room-level　occupancy　accurately　with　commodity　WiFi　infrastructure.　Depending　on　the　needs　of　different　occupancy　information　subscribers,　our　system　is　flexible　enough　to　switch　between　snapshot　estimation　mode　and　continuous　inference　mode,　to　trade　estimation　accuracy　for　delay　and　communication　cost.　We　evaluate　the　system　on　a　hardware　testbed　deployed　in　a　600m2　workspace　with　25　occupants　for　6　weeks.　Experimental　results　show　that　the　proposed　system　significantly　outperforms　competing　systems　in　both　inference　accuracy　and　robustness.　©　2016　IEEE.