Adaptive Short-Temporal Induced Aware Fusion Network for Predicting Attention Regions Like a Driver
Qiang Li, Chunsheng Liu, Faliang Chang, Shuang Li, Hui Liu, Zehao Liu · IEEE Transactions on Intelligent Transportation Systems · 2022
Driver attention prediction can solve the problem of ‘Where should the driver pay attention?’, Most previous methods are designed to predict regional attention with redundant regions. Furthermore, popular spatial-temporal feature extraction networks such as ConvLSTM and 3D-CNN are difficult to achieve real-time. To overcome these difficulties, we propose an Adaptive Short-temporal Induced Aware Fusion Network (ASIAF-Net) for region-level and object-level driver attention prediction.1In ASIAF-Net, we design anAttention Related Spatial Feature Encoder(AF-Encoder) and anInduced Aware Fusion Network(IAF-Net) as the main network; with anAssociation Analysis Cell(AAC), the AF-Encoder makes it possible to effectively capture the relationship information of different objects. Considering most vital visual cues from moving objects, we propose aSelf-adaptive Short-temporal Feature Extraction Module(SSFE-Module) to obtain inter-frame motion features. In IAF-Net, aMulti-scale Driver Attention Region Prediction Branchis designed to predict the regional attention, and anObject Saliency Estimation Branchis proposed to fuse the perception results and the regional attention map to estimate the object-level attention. Experiments show that the proposed ASIAF-Net can predict driver’s attention on regions and objects more robustly and precisely than state-of-the-art methods on three datasets, and that it achieves real-time on our ADAS platform.