Real-Time Visual Subject Tracking and Classification by
Combining Motion Signal Analysis and Tridimensional - Shape Feature Classifiers
with Group-Induction Boosting Algorithms
This paper provides a novel and
unprecedented approach for integrating motion features in the detection and
classification of moving subjects in a static environment. More specifically,
we measure the impact of the use of trajectory history, rotation history, blob
orientation, motion frequency in the three axes, motion acceleration,
segmentation errors, and flickering scores, and how they can influence
classification of moving people, pets, and other objects. We apply our method
to data captured by a combined color and depth camera sensor. We find that,
while some motion descriptors slightly improve accuracy, the use of them in
conjunction outperforms previous approaches in the classification and tracking
of real-world moving subjects in real-time.
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