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On-board vision based traffic risk assessment is a challenging task for intelligent driving systems, which has some special challenges including spatial-temporal feature extraction, different judgements of risks, real-time requirement, lacking data, etc. To overcome these difficulties, we propose a novel Temporal Shift and Spatial Attention based Two-stream Network (TSSAT-Net) for on-board vision based traffic risk assessment. Firstly, we build new judgement measures that integrate actual driving experience and scenario complexity, and release an on-board vision based traffic risk assessment dataset. Secondly, a novel weighted Temporal Shift Module (weighted-TSM) based two-stream network is proposed; unlike previous methods that rely on complex and time-consuming 3D CNN or LSTM calculation, the proposed two-stream network can effectively extract spatial-temporal features using a weighted temporal shift mechanism with just 2D CNN calculation requirement. Thirdly, a spatial and channel attention mechanism is proposed to make the TSSAT-Net more focus on the features closely related to traffic risks, avoiding redundant information in complex traffic scenarios. Experiments based on the released comprehensive dataset show that our method achieves the state-of-the-art classification accuracy in real-time. [ABSTRACT FROM AUTHOR]

Machine vision based vehicle counting and traffic flow estimation are challenging problems especially for dense traffic scenarios. Previous line of interest (LOI) counting methods rarely focus on dense scenarios and their performance largely relies on the accuracy of tracking. Avoiding the use of complex tracking methods, an LOI counting framework is proposed to address the bi-directional LOI counting problem in dense scenarios. There are three main contributions. Firstly, instead of treating the LOI vehicle counting problem as a combination of detecting and tracking of individual vehicles, the bi-directional traffic flow is taken as a whole and a novel spatio-temporal counting feature (STCF) is proposed for extracting bi-directional traffic flow features in dense traffic scenarios. Secondly, without relying on a multi-target tracking process for tracking and counting each vehicle, a counting network is proposed, called the counting Long Short-Term Memory (cLSTM) network, to do analysis of the bi-directional STCF features and vehicle counting in successive video frames. Lastly, an estimation model is designed for estimating traffic flow parameters including speed, volume and density. Experiments performed on the UA-DETRAC dataset and the captured videos show that the proposed vehicle counting method outperforms the tested representative LOI counting methods in both accuracy and speed, and that the proposed framework can efficiently estimate traffic flow parameters including speed, volume and density in real time. [ABSTRACT FROM AUTHOR]