Currently,　intelligent　security　systems　are　widely　deployed　in　indoor　buildings　to　ensure　the　safety　of　people　in　shopping　malls,　banks,　train　stations,　and　other　indoor　buildings.　Multi-Object　Tracking　(MOT),　as　an　important　component　of　intelligent　security　systems,　has　received　much　attention　from　many　researchers　in　recent　years.　However,　existing　multi-objective　tracking　algorithms　still　suffer　from　trajectory　drift　and　interruption　problems　in　crowded　scenes,　which　cannot　provide　valuable　data　for　managers.　In　order　to　solve　the　above　problems,　this　paper　proposes　a　Multi-Object　Tracking　algorithm　for　RGB-D　images　based　on　Asymmetric　Dual　Siamese　networks　(ADSiamMOT-RGBD).　This　algorithm　combines　appearance　information　from　RGB　images　and　target　contour　information　from　depth　images.　Furthermore,　the　attention　module　is　applied　to　repress　the　redundant　information　in　the　combined　features　to　overcome　the　trajectory　drift　problem.　We　also　propose　a　trajectory　analysis　module,　which　analyzes　whether　the　head　movement　trajectory　is　correct　in　combination　with　time-context　information.　It　reduces　the　number　of　human　error　trajectories.　The　experimental　results　show　that　the　proposed　method　in　this　paper　has　better　tracking　quality　on　the　MICC,　EPFL,　and　UMdatasets　than　the　previous　work.