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.