کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4969035 | 1449848 | 2017 | 32 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Comparing random forest approaches to segmenting and classifying gestures
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
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چکیده انگلیسی
A complete gesture recognition system should localize and classify each gesture from a given gesture vocabulary, within a continuous video stream. In this work, we compare two approaches: a method that performs the tasks of temporal segmentation and classification simultaneously with another that performs the tasks sequentially. The first method trains a single random forest model to recognize gestures from a given vocabulary, as presented in a training dataset of video plus 3D body joint locations, as well as out-of-vocabulary (non-gesture) instances. The second method employs a cascaded approach, training a binary random forest model to distinguish gestures from background and a multi-class random forest model to classify segmented gestures. Given a test input video stream, both frameworks are applied using sliding windows at multiple temporal scales. We evaluated our formulation in segmenting and recognizing gestures from two different benchmark datasets: the NATOPS dataset of 9600 gesture instances from a vocabulary of 24 aircraft handling signals, and the ChaLearn dataset of 7754 gesture instances from a vocabulary of 20Â Italian communication gestures. The performance of our method compares favorably with state-of-the-art methods that employ Hidden Markov Models or Hidden Conditional Random Fields on the NATOPS dataset. We conclude with a discussion of the advantages of using our model for the task of gesture recognition and segmentation, and outline weaknesses which need to be addressed in the future.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Image and Vision Computing - Volume 58, February 2017, Pages 86-95
Journal: Image and Vision Computing - Volume 58, February 2017, Pages 86-95
نویسندگان
Ajjen Joshi, Camille Monnier, Margrit Betke, Stan Sclaroff,