Recognition of Texting-While-Walking by Joint Features Based on Arm and Head Poses

Pedestrians “texting-while-walking” increase the risk of traffic accidents, since they are often not paying attention to their surrounding environments and fails to notice approaching vehicles. Thus, the recognition of texting-while-walking from an in-vehicle camera should be helpful for safety driving assistance. In this paper, we propose a method to recognize a pedestrian texting-while-walking from in-vehicle camera images. The proposed approach focuses on the characteristic relationship between the arm and the head poses observed during a texting-while-walking behavior. In this paper, Pose-Dependent Joint HOG feature is proposed as a novel feature, which uses parts locations as prior knowledge and describes the cooccurrence of the arm and the head poses. To show the effectiveness of the proposed method, we constructed a dataset and evaluated it.

[1]  Shigeru Haga,et al.  Effects of using a Smart Phone on Pedestrians’ Attention and Walking☆ , 2015 .

[2]  M.M. Trivedi,et al.  Image based estimation of pedestrian orientation for improving path prediction , 2008, 2008 IEEE Intelligent Vehicles Symposium.

[3]  Hironobu Fujiyoshi,et al.  Object detection by joint features based on two-stage boosting , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[4]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Despina Stavrinos,et al.  Distracted walking: cell phones increase injury risk for college pedestrians. , 2011, Journal of safety research.

[6]  Reinhard Klette,et al.  Part-Based RDF for Direction Classification of Pedestrians, and a Benchmark , 2014, ACCV Workshops.

[7]  Pietro Perona,et al.  Fast Feature Pyramids for Object Detection , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[9]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[10]  T. Poggio,et al.  Direction estimation of pedestrian from multiple still images , 2004, IEEE Intelligent Vehicles Symposium, 2004.