Estimating Speeds and Directions of Pedestrians in Real-Time Videos: A solution to Road-Safety Problem

Pedestrian injuries and fatalities are one of the most significant problems related to travel and road safety. Pedestrians are vulnerable users of roads and due to the very different velocities and mass when compared to vehicles like cars and trucks, and very often they undergo serious injuries in case of collisions. Older pedestrians are even more vulnerable to injuries and fatalities due to (i) their reduced mobility and reflexes and (ii) their increased fragility when compared to young individuals. Crosswalks are the point where pedestrians face lower level of safety because they have to cross the street and must be aware of the incoming traffic. Such kind of awareness becomes difficult in case of old pedestrians because of their reduced physical and perceptive capabilities. Besides other factors, lower speed of an old pedestrian is an important factor that limits the mobility of old pedestrians and it also increases the risk of fatalities while crossing the road. In this paper, we developed vision based intelligent system that can detect low speeds and directions of pedestrians and can help him/her by (a) increasing the time associated to a green light for pedestrians, (b) using audible signals to help the pedestrians understanding that there are cars approaching the crossing.

[1]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[2]  Carol Holland,et al.  Gender differences in factors predicting unsafe crossing decisions in adult pedestrians across the lifespan: a simulation study. , 2010, Accident; analysis and prevention.

[3]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[4]  Tova Rosenbloom,et al.  Crossing at a red light: Behaviour of individuals and groups , 2009 .

[5]  Tamitza Toroyan,et al.  Global status report on road safety , 2009, Injury Prevention.

[6]  Cina Motamed,et al.  Monitoring pedestrians in a uncontrolled urban environment by matching low-level features , 1996, 1996 IEEE International Conference on Systems, Man and Cybernetics. Information Intelligence and Systems (Cat. No.96CH35929).

[7]  Thomas B. Moeslund,et al.  A Survey of Computer Vision-Based Human Motion Capture , 2001, Comput. Vis. Image Underst..

[8]  Jiri Matas,et al.  Forward-Backward Error: Automatic Detection of Tracking Failures , 2010, 2010 20th International Conference on Pattern Recognition.

[9]  John Whitelegg Quality of Life and Public Management: Redefining Development in the Local Environment , 2012 .

[10]  P. Mirchandani,et al.  Real-time detection of crossing pedestrians for traffic-adaptive signal control , 2004, Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749).

[11]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Vladimir Pavlovic,et al.  Visual Interpretation of Hand Gestures for Human-Computer Interaction: A Review , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[14]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[15]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Stefano Ghidoni,et al.  Vision-based monitoring of pedestrian crossings , 2007, 14th International Conference on Image Analysis and Processing (ICIAP 2007).

[17]  Richard Szeliski,et al.  Spline-Based Image Registration , 1997, International Journal of Computer Vision.

[18]  G Gallop SAFETY FOR SENIORS: FINAL REPORT ON PEDESTRIAN SAFETY , 1989 .

[19]  David J. Fleet,et al.  Performance of optical flow techniques , 1994, International Journal of Computer Vision.

[20]  Alessio Del Bue,et al.  Human behavior analysis in video surveillance: A Social Signal Processing perspective , 2013, Neurocomputing.

[21]  Navid Nourani-Vatani,et al.  A Study of feature extraction algorithms for optical flow tracking , 2012, ICRA 2012.

[22]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[23]  Xiaojuan Wu,et al.  Crowd foreground detection and density estimation based on moment , 2010, 2010 International Conference on Wavelet Analysis and Pattern Recognition.

[24]  W. Eric L. Grimson,et al.  Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[25]  Beat Fasel,et al.  Automati Fa ial Expression Analysis: A Survey , 1999 .

[26]  Mubarak Shah,et al.  A survey of motion analysis from moving light displays , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[27]  R. Ashworth,et al.  PEDESTRIAN SUBWAYS IN URBAN AREAS: SOME OBSERVATIONS CONCERNING THEIR USE , 1994 .

[28]  Osama Masoud,et al.  Vision-based monitoring of intersections , 2002, Proceedings. The IEEE 5th International Conference on Intelligent Transportation Systems.

[29]  Margaret M. Peden,et al.  World Report on Road Traffic Injury Prevention , 2004 .