Vanishing Point-Based Image Transforms for Enhancement of Probabilistic Occupancy Map-Based People Localization

The widespread use of vision-based surveillance systems has inspired many research efforts on people localization. In this paper, a series of novel image transforms based on the vanishing point of vertical lines is proposed for enhancement of the probabilistic occupancy map (POM)-based people localization scheme. Utilizing the characteristic that the extensions of vertical lines intersect at a vanishing point, the proposed transforms, based on image or ground plane coordinate system, aims at producing transformed images wherein each standing/walking person will have an upright appearance. Thus, the degradation in localization accuracy due to the deviation of camera configuration constraint specified can be alleviated, while the computation efficiency resulted from the applicability of integral image can be retained. Experimental results show that significant improvement in POM-based people localization for more general camera configurations can indeed be achieved with the proposed image transforms.

[1]  Daniel González-Jiménez,et al.  Toward Pose-Invariant 2-D Face Recognition Through Point Distribution Models and Facial Symmetry , 2007, IEEE Transactions on Information Forensics and Security.

[2]  Andrew Zisserman,et al.  Learning To Count Objects in Images , 2010, NIPS.

[3]  Ramakant Nevatia,et al.  Segmentation and Tracking of Multiple Humans in Crowded Environments , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Luis E. Ortiz,et al.  Who are you with and where are you going? , 2011, CVPR 2011.

[5]  Jen-Hui Chuang,et al.  Vanishing Point-based Line Sampling for Real-time People Localization , 2013, IEEE Transactions on Circuits and Systems for Video Technology.

[6]  Christophe De Vleeschouwer,et al.  Detection and recognition of sports(wo)men from multiple views , 2009, 2009 Third ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC).

[7]  S. Vassilopouloua,et al.  Orthophoto generation using IKONOS imagery and high-resolution DEM : a case study on volcanic hazard monitoring of Nisyros Island ( Greece ) , 2002 .

[8]  Pascal Fua,et al.  Robust People Tracking with Global Trajectory Optimization , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[9]  Shaogang Gong,et al.  Tracking multiple people with a multi-camera system , 2001, Proceedings 2001 IEEE Workshop on Multi-Object Tracking.

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

[11]  Sergiu Nedevschi,et al.  Stereo-Based Pedestrian Detection for Collision-Avoidance Applications , 2009, IEEE Transactions on Intelligent Transportation Systems.

[12]  King Ngi Ngan,et al.  Segmentation and Tracking Multiple Objects Under Occlusion From Multiview Video , 2011, IEEE Transactions on Image Processing.

[13]  Mubarak Shah,et al.  Tracking Multiple Occluding People by Localizing on Multiple Scene Planes , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Ákos Utasi,et al.  A Bayesian Approach on People Localization in Multicamera Systems , 2013, IEEE Transactions on Circuits and Systems for Video Technology.

[15]  Ákos Utasi,et al.  A 3-D marked point process model for multi-view people detection , 2011, CVPR 2011.

[16]  Michael S. Brown,et al.  Geometric and shading correction for images of printed materials using boundary , 2006, IEEE Transactions on Image Processing.

[17]  Pascal Fua,et al.  Ieee Transactions on Pattern Analysis and Machine Intelligence 1 Multiple Object Tracking Using K-shortest Paths Optimization , 2022 .

[18]  Ivan Laptev,et al.  Density-aware person detection and tracking in crowds , 2011, ICCV.

[19]  Afshin Dehghan,et al.  Part-based multiple-person tracking with partial occlusion handling , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[20]  Pascal Fua,et al.  Multicamera People Tracking with a Probabilistic Occupancy Map , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  L. Davis,et al.  M2Tracker: A Multi-View Approach to Segmenting and Tracking People in a Cluttered Scene , 2003, International Journal of Computer Vision.

[22]  Dariu Gavrila,et al.  Multi-person Localization and Track Assignment in Overlapping Camera Views , 2011, DAGM-Symposium.

[23]  Jen-Hui Chuang,et al.  Vanishing point-based line sampling for efficient axis-based people localization , 2011, 2011 18th IEEE International Conference on Image Processing.

[24]  K. Otsuka,et al.  Multiview occlusion analysis for tracking densely populated objects based on 2-D visual angles , 2004, CVPR 2004.

[25]  Francisco José Madrid-Cuevas,et al.  People detection and tracking with multiple stereo cameras using particle filters , 2009, J. Vis. Commun. Image Represent..

[26]  Wilfried Philips,et al.  Dempster-Shafer based multi-view occupancy maps , 2010 .

[27]  Naoki Mukawa,et al.  Multiview occlusion analysis for tracking densely populated objects based on 2-D visual angles , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[28]  Yael Moses,et al.  Tracking in a Dense Crowd Using Multiple Cameras , 2010, International Journal of Computer Vision.

[29]  Jürgen Friedrich,et al.  A new differential geometric method to rectify digital images of the Earth's surface using isothermal coordinates , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[30]  Hua-Tsung Chen,et al.  VP-transform: A novel vanishing point-based image transform for enhancement of people localization , 2013, 2013 IEEE International Conference on Multimedia and Expo (ICME).

[31]  Wen Gao,et al.  Locally Linear Regression for Pose-Invariant Face Recognition , 2007, IEEE Transactions on Image Processing.

[32]  Yael Moses,et al.  Homography based multiple camera detection and tracking of people in a dense crowd , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[33]  Yihong Gong,et al.  Human Tracking Using Convolutional Neural Networks , 2010, IEEE Transactions on Neural Networks.

[34]  Pascal Fua,et al.  Tracking multiple people under global appearance constraints , 2011, 2011 International Conference on Computer Vision.

[35]  Ioannis Pratikakis,et al.  Goal-Oriented Rectification of Camera-Based Document Images , 2011, IEEE Transactions on Image Processing.

[36]  B. Leibe,et al.  Taking Mobile Multi-object Tracking to the Next Level: People, Unknown Objects, and Carried Items , 2012, ECCV.

[37]  Alberto Elfes,et al.  Using occupancy grids for mobile robot perception and navigation , 1989, Computer.

[38]  David S. Doermann,et al.  Geometric Rectification of Camera-Captured Document Images , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[39]  David Suter,et al.  Adaptive Object Tracking Based on an Effective Appearance Filter , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[40]  Yu Zhang,et al.  Ieee Transactions on Pattern Analysis and Machine Intelligence 1 an Improved Physically-based Method for Geometric Restoration of Distorted Document Images , 2007 .