During object tracking

motion information can predict the location of the object. If motion information is ignored or motion is inaccurately modeled

then tracking may fail. To deal with this issue

we introduce visual saliency

which can quickly capture the interesting object

in tracking. Furthermore

we propose a tracking algorithm based on spatio-temporal motion saliency. First

we propose a bottom-up computational model for spatio-temporal motion saliency according to the hierarchical motion processing in the visual cortex. We adopt 3D spatio-temporal filters for the coding of underlying motion signals and max-pooling operation for the coding of local features. Considering the temporal relationship between the spatio-temporal motion features in the historical and current frames

we construct the spatio-temporal motion saliency map by measuring the difference between consecutive frames. Second

in the frame of particle filter

we measure the correlation between the predictive state and the observation by combining spatio-temporal motion saliency with color histogram. The object state can then be determined and tracked. Compared with other methods

our approach can stably track the objects under unfavorable situations

such as variable lighting

background clutter

motion blurs

occlusion

and deformation. We can improve the tracking performance in terms of central position error

precision

and success rate. In addition

we integrate the spatio-temporal motion saliency into other tracking methods and achieve better results

which demonstrates the effectiveness of the spatio-temporal motion saliency for object tracking. The spatio-temporal motion saliency can improve tracking performance as it measures motion information effectively

thereby enhancing the salient area and suppressing interference.