A MOTION ATTENTION MODEL BASED ON RARITY WEIGHTING AND MOTION CUES IN DYNAMIC SCENES
Nowadays, motion attention model is a controversial topic in the biological computer vision area. The computational attention model can be decomposed into a set of features via pre-defined channels. Here we designed a bio-inspired vision attention model, and added the rarity measurement onto it. The priority of rarity is emphasized under the assumption of weighting effect upon the features logic fusion. At this stage, a final saliency map at each frame is adjusted by the spatiotemporal and rarity values. By doing this, the process of mimicking human vision attention becomes more realistic and logical to the real circumstance. The experiments are conducted on the benchmark dataset of static images and video sequences. We simulated the attention shift based on several dataset. Most importantly, our dynamic scenes are mostly selected from the objects moving on the highway and dynamic scenes. The former one can be developed on the detection of car collision and will be a useful tool for further application in robotics. We also conduct experiment on the other video clips to prove the rationality of rarity factor and feature cues fusion methods. Finally, the evaluation results indicate our visual attention model outperforms several state-of-the-art motion attention models.
rarity weighting dynamic scenes visual attention model motion vector fields