In this traffic-scene-modeling study, we propose an image-captioning network which incorporates element attention into an encoder-decoder mechanism to generate more reasonable scene captions. A visual-relationship-detecting network is also developed to detect the relative positions of object pairs. Firstly, the traffic scene elements are detected and segmented according to their clustered locations. Then, the image-captioning network is applied to generate the corresponding description of each traffic scene element. The visual-relationship-detecting network is utilized to detect the position relations of all object pairs in the subregion. The static and dynamic traffic elements are appropriately selected and organized to construct a 3D model according to the captions and the position relations. The reconstructed 3D traffic scenes can be utilized for the offline test of unmanned vehicles. The evaluations and comparisons based on the TSD-max, KITTI and Microsoft’s COCO datasets demonstrate the effectiveness of the proposed framework.

Leveraging the consensus and complementary principle to find a common representation for different views is an essential problem of multi-view clustering. To address the problem, many Low-Rank Representation (LRR) based methods have been proposed. However, existing LRR based methods have two common limitations: 1) they adopt graph regularization that only considers simple pairwise similarities among data points, and 2) they do not generally characterize the importance of each view. In this letter, we correspondingly utilize hypergraph regularization and a self-weighting strategy to handle the limitations with an LRR based model. Specifically, in our model, we construct hypergraph Laplacian matrices of each view that explicitly contain high order relations among data points, to improve the usage of complementary information. Meanwhile, the self-weighting strategy that preserves view specific information and assigns adaptive weights to each view is leveraged to take full advantage of multi-view consensus information. Based on the Augmented Lagrangian Multiplier (ALM) scheme, we design an effective alternating iterative strategy to optimize the model. Extensive experiments conducted on four benchmark datasets validate the superiority of our method.