ISSN : 1796-2048
Volume : 2    Issue : 4    Date : August 2007

Flux Tensor Constrained Geodesic Active Contours with Sensor Fusion for Persistent Object Tracking
Filiz Bunyak, Kannappan Palaniappan, Sumit Kumar Nath, and Gunasekaran Seetharaman
Page(s): 20-33
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This paper makes new contributions in motion detection, object segmentation and trajectory estimation
to create a successful object tracking system. A new efficient motion detection algorithm referred to as the
flux tensor is used to detect moving objects in infrared video without requiring background modeling or
contour extraction. The flux tensor-based motion detector when applied to infrared video is more accurate
than thresholding ”hot-spots”, and is insensitive to shadows as well as illumination changes in the
visible channel. In real world monitoring tasks fusing scene information from multiple sensors and
sources is a useful core mechanism to deal with complex scenes, lighting conditions and environmental
variables. The object segmentation algorithm uses level set-based geodesic active contour evolution that
incorporates the fusion of visible color and infrared edge informations in a novel manner. Touching or
overlapping objects are further refined during the segmentation process using an appropriate
shapebased model. Multiple object tracking using correspondence graphs is extended to handle groups
of objects and occlusion events by Kalman filter-based cluster trajectory analysis and watershed
segmentation. The proposed object tracking algorithm was successfully tested on several difficult
outdoor multispectral videos from stationary sensors and is not confounded by shadows or illumination

Index Terms
Flux tensor, sensor fusion, object tracking, active contours, level set, infrared images.