Abstract
This paper presents a framework for spotting and recognizing continuous human gestures. Skeleton based features are extracted from normalized human body coordinates to represent gestures. These features are then used to construct spatio-temporal template based Random Decision Forest models. Finally, predictions from different models are fused at decision-level to improve overall recognition performance. Our method has shown competitive results on the ChaLearn 2014 Looking at People: Gesture Recognition dataset. Trained on a dataset of 20 gesture vocabulary and 7754 gesture samples, our method achieved a Jaccard Index of \(0.74663\) on the test set, reaching 7th place among contenders. Among methods that exclusively used skeleton based features, our method obtained the highest recognition performance.
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Camgöz, N.C., Kindiroglu, A.A., Akarun, L. (2015). Gesture Recognition Using Template Based Random Forest Classifiers. In: Agapito, L., Bronstein, M., Rother, C. (eds) Computer Vision - ECCV 2014 Workshops. ECCV 2014. Lecture Notes in Computer Science(), vol 8925. Springer, Cham. https://doi.org/10.1007/978-3-319-16178-5_41
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