Independent increment processes for human motion recognition
Computer Vision and Image Understanding
Tracking the Left Ventricle in Ultrasound Images Based on Total Variation Denoising
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
Target tracking for mobile robot platforms via object matching and background anti-matching
Robotics and Autonomous Systems
A graph-based feature combination approach to object tracking
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
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Many object-tracking algorithms are based on low-level features detected in the image. Typically, the object shape and position are estimated to fit the observed features. Unfortunately, image analysis methods often produce invalid features (outliers) which do not belong to the object boundary. These features have a strong influence on the shape estimates, leading to meaningless tracking results. This paper proposes a robust tracking algorithm which is able to deal with outliers, inspired in the probabilistic data association filter proposed in the context of point tracking. The algorithm is based on two key concepts. First, middle level features (strokes) are used instead of low-level ones (edge points). Second, two labels (valid/invalid) are considered for each stroke. Since the stroke labels are unknown all labeling sequences are considered and a probability (confidence degree) is assigned to each of them. In this way, all the strokes contribute to track the moving object but with different weights. This allows a robust performance of the tracker in the presence of outliers. Experimental tests are provided to assess the performance of the proposed algorithm in lip and gesture tracking and surveillance applications.