A key frame extraction algorithm based on the visual attention model is proposed for lane surveillance video. First

the top-down method is used to detect moving objects whose position saliency is decided by the clearest position of license plates and vehicles. Then

within the moving objects

the bottom-up method is used to calculate the moving orientation and moving intensity saliency of these moving objects. Next

the visual attention curve is fused by a simple adaptive linear mode. Last a derivative curve is generated

from which the frame with the most salient value in those zero-crossing points from the positive to the negative on the derivative curve is selected as key frame. Experiments show that the key frames extracted by the proposed algorithm not only include the optimal or suboptimal positions of all passed vehicles

but also include on-street parking

speeding

reverse driving

and other traffic incidents. The results are consistent with the traffic observers’ visual perception and conducive to the extraction of vehicle static features to form the traffic video features database.