Conclusion and future work
In this article, we focus on the vehicle tracking task for IOV and propose the SAS-Net model which combines the bilinear network with a visual attention mechanism, making the model select the features from different channels according to different impacts of features from semantic areas. The model makes the vehicle no longer disturbed by the background area and the occluded vehicles and has obtained good results compared with other methods. The success rate of SAS-Net tracking is higher than that of KCF, DSST, and Siamese-FC under most overlapping rate thresholds. The precision of SAS-Net is much higher than the other three methods when the distance threshold is below 30. Our SAS-Net achieves a real-time tracking speed. If there is more hardware support, this model can be applied to largescale intelligent IOV system to assist all the connected vehicles to make a good travel plan. In the future research work, the joint vehicle tracking method based on the multi-image sensor will be brilliant. In the IOV system, interaction and fusion of information generated by different sensors among different vehicles are inevitable. In the combination of multi-sensors, how to fuse the multimodal data from various kinds of sensors, such as image sensors and wireless sensors, will be a long-term research goal. Deep neural networks still have great potential in these fields. Designing different kinds of neural networks for these multimodal data can achieve the fusion of different sensor information.
