Spatiotemporal Analysis of Static and Dynamic Traffic Elements From Road Scenes

Spatiotemporal analysis of road scenes is a hot research topic in the communities of computer vision and intelligent transportation systems. In this paper, we propose a new framework for spatiotemporal analysis of static and dynamic traffic elements from road scenes. In the first stage, a bottom-up analysis method for static traffic elements is proposed based on a hierarchical spatiotemporal model using hidden conditional random fields (HCRF). The bottom-level features are extracted from sub-regions in the hierarchical model, and the local and global features of the image sequence are then fully combined for spatial and temporal layers. In the second stage, a lightweight multi-stream 3DCNN network is developed for the behavior classification of dynamic traffic elements, which is composed of three parts. Firstly, a SELayer-3DCNN is designed to extract the appearance, motion and edge information from the image sequences. Secondly, the channel attention fusion strategy (CAF) is introduced to enhance the feature fusion ability. Finally, the 3D-RFB module is incorporated to expand the receptive field of the convolution kernel. The experimental results well demonstrate the effectiveness of the proposed framework.

Automated summary

This paper proposes a new framework for spatiotemporal analysis of static and dynamic traffic elements from road scenes. It applies a hierarchical approach combined with hidden conditional random fields (HCRF) to analyze the static traffic elements, and a lightweight multi-stream 3DCNN network for the behavior classification of dynamic traffic elements. Experimental results demonstrate the effectiveness of the proposed framework.