DADA-2000: Can Driving Accident be Predicted by Driver Attentionƒ Analyzed by A Benchmark

Driver attention prediction is currently becoming the focus in safe driving research community, such as the DR(eye)VE project and newly emerged Berkeley DeepDrive Attention (BDD-A) database in critical situations. In safe driving, an essential task is to predict the incoming accidents as early as possible. BDD-A was aware of this problem and collected the driver attention in laboratory because of the rarity of such scenes. Nevertheless, BDD-A focuses the critical situations which do not encounter actual accidents, and just faces the driver attention prediction task, without a close step for accident prediction. In contrast to this, we explore the view of drivers’ eyes for capturing multiple kinds of accidents, and construct a more diverse and larger video benchmark than ever before with the driver attention and the driving accident annotation simultaneously (named as DADA-2000), which has 2000 video clips owning about 658, 476 frames on 54 kinds of accidents. These clips are crowd-sourced and captured in various occasions (highway, urban, rural, and tunnel), weather (sunny, rainy and snowy) and light conditions (daytime and nighttime). For the driver attention representation, we collect the maps of fixations, saccade scan path and focusing time. The accidents are annotated by their categories, the accident window in clips and spatial locations of the crash-objects. Based on the analysis, we obtain a quantitative and positive answer for the question in this paper.

[1]  Yutaka Satoh,et al.  Drive Video Analysis for the Detection of Traffic Near-Miss Incidents , 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[2]  Min Sun,et al.  Anticipating Accidents in Dashcam Videos , 2016, ACCV.

[3]  Andrea Palazzi,et al.  Learning where to attend like a human driver , 2016, 2017 IEEE Intelligent Vehicles Symposium (IV).

[4]  Mubarak Shah,et al.  Real-World Anomaly Detection in Surveillance Videos , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[5]  Byeongkeun Kang,et al.  A computational framework for driver's visual attention using a fully convolutional architecture , 2017, 2017 IEEE Intelligent Vehicles Symposium (IV).

[6]  David Whitney,et al.  Predicting Driver Attention in Critical Situations , 2017, ACCV.

[7]  Mohak Shah,et al.  Is it Safe to Drive? An Overview of Factors, Challenges, and Datasets for Driveability Assessment in Autonomous Driving , 2018, ArXiv.

[8]  Cui-Hua Li,et al.  Unifying visual saliency with HOG feature learning for traffic sign detection , 2009, 2009 IEEE Intelligent Vehicles Symposium.

[9]  John K. Tsotsos,et al.  Agreeing to cross: How drivers and pedestrians communicate , 2017, 2017 IEEE Intelligent Vehicles Symposium (IV).

[10]  Qi Wang,et al.  Online Anomaly Detection in Crowd Scenes via Structure Analysis , 2015, IEEE Transactions on Cybernetics.

[11]  Yutaka Satoh,et al.  Anticipating Traffic Accidents with Adaptive Loss and Large-Scale Incident DB , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[12]  Andrea Palazzi,et al.  Predicting the Driver's Focus of Attention: The DR(eye)VE Project , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  George Papandreou,et al.  Rethinking Atrous Convolution for Semantic Image Segmentation , 2017, ArXiv.

[14]  Cheng-Lin Liu,et al.  Traffic Sign Detection Using a Cascade Method With Fast Feature Extraction and Saliency Test , 2017, IEEE Transactions on Intelligent Transportation Systems.

[15]  Manuel Drees,et al.  Dark Matter Theory , 2018, Proceedings of The 39th International Conference on High Energy Physics — PoS(ICHEP2018).

[16]  Qi Wang,et al.  Incrementally perceiving hazards in driving , 2018, Neurocomputing.

[17]  Bolei Zhou,et al.  Scene Parsing through ADE20K Dataset , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  John K. Tsotsos,et al.  Joint Attention in Driver-Pedestrian Interaction: from Theory to Practice , 2018, ArXiv.

[19]  Tao Deng,et al.  Where Does the Driver Look? Top-Down-Based Saliency Detection in a Traffic Driving Environment , 2016, IEEE Transactions on Intelligent Transportation Systems.

[20]  Wilson S. Geisler,et al.  Gaze-contingent real-time simulation of arbitrary visual fields , 2002, IS&T/SPIE Electronic Imaging.

[21]  Shenghua Gao,et al.  A Revisit of Sparse Coding Based Anomaly Detection in Stacked RNN Framework , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[22]  Yu Yao,et al.  Unsupervised Traffic Accident Detection in First-Person Videos , 2019, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).