As the target-tracking in coal mines using a single-cue lacks discrimination of target features and strategies using the multi-cue fusion lack the adaptation to changes of scene, a novel particle filter algorithm based on adaptive multi-cue fusion models was proposed for object-tracking.An optical flow histogram was established based on particle motion, then,the optical flow was fused with color information to obtain a multi-cue based observation model. An adaptive strategy of observation model weights was implemented by taking the centroid distance between the single-cue observation model and multi-cue fusion model as the contribution factor of the single-cue observation model. When it was implemented, the particle observation model would change with the object characteristics.The particle re-sampling was achieved by a proposal re-sampling when weights of single-cue observation model were all below a threshold. The results show that the tracking algorithm is an effective solution to tracking failure due to changes of scene in coal mines.The accuracy of estimation has increased by 1.57 times as compared with those of other particle filter algorithms.