Tracking of Multiple Humans in Meetings

Tracking conferees in meeting rooms is important for many applications. In this paper, we present an approach based on single-frame head-shoulder detection to track multiple humans in meetings. The responses of a multiple view head-shoulder detection system are taken as the observation of the human hypotheses. Trajectory initialization and termination are fully automatic and rely on the evidence collected from the detection responses. An object is tracked by data association if its corresponding detection response can be found; otherwise it is tracked by a meanshift style tracker. Finally the tracked hypotheses are verified by evidence collected from body part movements. The system is evaluated on two meeting video corpora.

[1]  Gerhard Rigoll,et al.  Face tracking in meeting room scenarios using omnidirectional views , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[2]  Samy Bengio,et al.  Modeling human interaction in meetings , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[3]  Ramakant Nevatia,et al.  Detection of multiple, partially occluded humans in a single image by Bayesian combination of edgelet part detectors , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[4]  Stephen J. McKenna,et al.  Head Tracking and Action Recognition in a Smart Meeting Room , 2003 .

[5]  Stanislav Sumec,et al.  Participant activity detection by hands and face movement tracking in the meeting room , 2004, Proceedings Computer Graphics International, 2004..

[6]  Ramakant Nevatia,et al.  Combined face-body tracking in indoor environment , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[7]  Larry S. Davis,et al.  Closely coupled object detection and segmentation , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[8]  Gerhard Rigoll,et al.  Face tracking in meeting room scenarios using omnidirectional views , 2004, ICPR 2004.

[9]  Ramakant Nevatia,et al.  Tracking of Multiple, Partially Occluded Humans based on Static Body Part Detection , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[10]  Dorin Comaniciu,et al.  The Variable Bandwidth Mean Shift and Data-Driven Scale Selection , 2001, ICCV.

[11]  Jean-Marc Odobez,et al.  Tracking People in Meetings with Particles , 2005 .

[12]  Martial Michel,et al.  The NIST Meeting Room Pilot Corpus , 2004, LREC.

[13]  Berna Erol,et al.  Segmenting People in Meeting Videos Using Mixture Background and Object Models , 2002, IEEE Pacific Rim Conference on Multimedia.

[14]  Rainer Stiefelhagen,et al.  Tracking focus of attention in meetings , 2002, Proceedings. Fourth IEEE International Conference on Multimodal Interfaces.