Online Spatial-temporal Data Fusion for Robust Adaptive Tracking

One problem with the adaptive tracking is that the data that are used to train the new target model often contain errors and these errors will affect the quality of the new target model. As time passes by, these errors will accumulate and eventually lead the tracker to drift away. In this paper, we propose a new method based on online data fusion to alleviate this tracking drift problem. Based on combining the spatial and temporal data through a dynamic Bayesian network, the proposed method can improve the quality of online data labeling, therefore minimizing the error associated with model updating and alleviating the tracking drift problem. Experiments show the proposed method significantly improves the performance of an existing adaptive tracking method.