Static force field representation of environments based on agents’ nonlinear motions

This paper presents a methodology that aims at the incremental representation of areas inside environments in terms of attractive forces. It is proposed a parametric representation of velocity fields ruling the dynamics of moving agents. It is assumed that attractive spots in the environment are responsible for modifying the motion of agents. A switching model is used to describe near and far velocity fields, which in turn are used to learn attractive characteristics of environments. The effect of such areas is considered radial over all the scene. Based on the estimation of attractive areas, a map that describes their effects in terms of their localizations, ranges of action, and intensities is derived in an online way. Information of static attractive areas is added dynamically into a set of filters that describes possible interactions between moving agents and an environment. The proposed approach is first evaluated on synthetic data; posteriorly, the method is applied on real trajectories coming from moving pedestrians in an indoor environment.

[1]  F. J. Brockway Essentials of Physics , 1892, Glasgow Medical Journal.

[2]  P. Mahalanobis On the generalized distance in statistics , 1936 .

[3]  K. Lewin,et al.  Field Theory in Social Science: Selected Theoretical Papers , 1951 .

[4]  M. Smith Field Theory in Social Science: Selected Theoretical Papers. , 1951 .

[5]  N. G. Parke,et al.  Ordinary Differential Equations. , 1958 .

[6]  P. Hartman Ordinary Differential Equations , 1965 .

[7]  Mary Hesse,et al.  Forces and Fields: The Concept of Action at a Distance in the History of Physics , 1965 .

[8]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[9]  Helbing,et al.  Social force model for pedestrian dynamics. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[10]  Jim Hunter,et al.  Knowledge-Based Event Detection in Complex Time Series Data , 1999, AIMDM.

[11]  Hans-Hellmut Nagel,et al.  Incremental recognition of traffic situations from video image sequences , 2000, Image Vis. Comput..

[12]  Dirk Helbing,et al.  Simulating dynamical features of escape panic , 2000, Nature.

[13]  Tieniu Tan,et al.  Semantic interpretation of object activities in a surveillance system , 2002, Object recognition supported by user interaction for service robots.

[14]  Padhraic Smyth,et al.  Segmental semi-markov models and applications to sequence analysis , 2002 .

[15]  Eamonn J. Keogh,et al.  Segmenting Time Series: A Survey and Novel Approach , 2002 .

[16]  Peter Norvig,et al.  Artificial intelligence - a modern approach, 2nd Edition , 2003, Prentice Hall series in artificial intelligence.

[17]  Tieniu Tan,et al.  Traffic accident prediction using 3-D model-based vehicle tracking , 2004, IEEE Transactions on Vehicular Technology.

[18]  Kenneth W. Johnson,et al.  Essentials of Physics , 2005 .

[19]  Tobias Bjerregaard,et al.  A survey of research and practices of Network-on-chip , 2006, CSUR.

[20]  Mohan M. Trivedi,et al.  Novel concepts and challenges for the next generation of video surveillance systems , 2007, Machine Vision and Applications.

[21]  M. Shah,et al.  Object tracking: A survey , 2006, CSUR.

[22]  Ahmad A. Masoud Decentralized Self-Organizing Potential Field-Based Control for Individually Motivated Mobile Agents in a Cluttered Environment: A Vector-Harmonic Potential Field Approach , 2007, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[23]  Huaiqing Wang,et al.  Novel Online Methods for Time Series Segmentation , 2008, IEEE Transactions on Knowledge and Data Engineering.

[24]  Jun-Wei Hsieh,et al.  Video-Based Human Movement Analysis and Its Application to Surveillance Systems , 2008, IEEE Transactions on Multimedia.

[25]  Dirk Helbing,et al.  Pedestrian, Crowd and Evacuation Dynamics , 2013, Encyclopedia of Complexity and Systems Science.

[26]  Nuno Vasconcelos,et al.  Anomaly detection in crowded scenes , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[27]  Mário A. T. Figueiredo,et al.  Trajectory Classification Using Switched Dynamical Hidden Markov Models , 2010, IEEE Transactions on Image Processing.

[28]  M. Elif Karsligil,et al.  Traffic event classification at intersections based on the severity of abnormality , 2011, Machine Vision and Applications.

[29]  Luis A. Aguirre,et al.  Flight path reconstruction – A comparison of nonlinear Kalman filter and smoother algorithms , 2011 .

[30]  W. Eric L. Grimson,et al.  Trajectory Analysis and Semantic Region Modeling Using Nonparametric Hierarchical Bayesian Models , 2011, International Journal of Computer Vision.

[31]  Irfan A. Essa,et al.  Gaussian process regression flow for analysis of motion trajectories , 2011, 2011 International Conference on Computer Vision.

[32]  Dietmar Bauer,et al.  Can Walking Behavior be Predicted?: Analysis of Calibration and Fit of Pedestrian Models , 2011 .

[33]  Mohan M. Trivedi,et al.  Trajectory Learning for Activity Understanding: Unsupervised, Multilevel, and Long-Term Adaptive Approach , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[34]  Gang Ye,et al.  A New Method for Piecewise Linear Representation of Time Series Data , 2012 .

[35]  Hans W. Guesgen,et al.  Situational Awareness for Assistive Technologies , 2012, Situational Awareness for Assistive Technologies.

[36]  H. W. Guesgen,et al.  Situational Awareness for Assistive Technologies - Volume 14 Ambient Intelligence and Smart Environments , 2012 .

[37]  Jie Yang,et al.  Push the limit of WiFi based localization for smartphones , 2012, Mobicom '12.

[38]  Fredrik Gustafsson,et al.  Recognition of Anomalous Motion Patterns in Urban Surveillance , 2013, IEEE Journal of Selected Topics in Signal Processing.

[39]  Manuel G. Penedo,et al.  Hierarchical framework for robust and fast multiple-target tracking in surveillance scenarios , 2013, Expert Syst. Appl..

[40]  Norbert Brändle,et al.  Validating social force based models with comprehensive real world motion data , 2014 .

[41]  Fabrizio Pancaldi,et al.  Map-Aware Models for Indoor Wireless Localization Systems: An Experimental Study , 2014, IEEE Transactions on Wireless Communications.

[42]  Francesco Palmieri,et al.  Abnormal vessel behavior detection in port areas based on Dynamic Bayesian Networks , 2014, 17th International Conference on Information Fusion (FUSION).

[43]  Carlo S. Regazzoni,et al.  Online pedestrian group walking event detection using spectral analysis of motion similarity graph , 2015, 2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).

[44]  Po-Ruey Lei,et al.  A framework for anomaly detection in maritime trajectory behavior , 2015, Knowledge and Information Systems.

[45]  Jesús García,et al.  Model-based trajectory reconstruction with IMM smoothing and segmentation , 2015, Inf. Fusion.

[46]  Xiaogang Wang,et al.  Understanding pedestrian behaviors from stationary crowd groups , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[47]  Meifang Li,et al.  The parameter calibration and optimization of social force model for the real-life 2013 Ya’an earthquake evacuation in China , 2015 .

[48]  Wei Shen,et al.  Abnormal events detection in crowded scenes by trajectory cluster , 2015, Precision Engineering Measurements and Instrumentation.

[49]  Carlo S. Regazzoni,et al.  Incremental learning of environment interactive structures from trajectories of individuals , 2016, 2016 19th International Conference on Information Fusion (FUSION).

[50]  Carlo S. Regazzoni,et al.  Online Nonparametric Bayesian Activity Mining and Analysis From Surveillance Video , 2016, IEEE Transactions on Image Processing.

[51]  Tadashi Koga,et al.  Real-time motion detection for high-assurance aircraft tracking system using Downlink Aircraft Parameters , 2016, Simul. Model. Pract. Theory.

[52]  Qi Zhang,et al.  Real-Time Automatic Obstacle Detection method for Traffic Surveillance in Urban Traffic , 2015, Journal of Signal Processing Systems.

[53]  Reprint of: Mahalanobis, P.C. (1936) "On the Generalised Distance in Statistics." , 2018, Sankhya A.