IEEE Transactions on Pattern Analysis and Machine Intelligence
Real Time Face and Object Tracking as a Component of a Perceptual User Interface
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
Tracking Multiple Humans in Complex Situations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Mean-Shift Tracking via a New Similarity Measure
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Coupled Parametric Active Contours
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Image Processing
Hi-index | 0.00 |
This paper tackles the problem of tracking multiple articulated objects undergoing frequent contacts in a video sequence. Conventional tracking methods usually fail to distinguish objects during contact, implying the use of disambiguation techniques to recover the identity of each object. Moreover, such methods do not provide detailed shape information at the articulation level. We address these limitations by proposing a novel approach to track multiple articulated objects using the mean-shift technique and physics engines. By defining a model of each object using geometrical primitives and physical constraints, we exploit the physics engine force solver as a control layer of the mean-shift process and follow the model and its deformation along the sequence. The method is applied to track mice observed with a webcam and two sporozoites observed in reflection interference contrast microscopy, highlighting the flexibility and genericity of the framework.