In this paper, we explore building classifiers to detect Salsa dance step primitives in choreographies available in the Huawei 3DLife data set. These can collectively be an important component of dance tuition systems that support e-learning. A dance step is reasoned as the shortest possible extract of bodily motion that can uniquely identify a particularly repeatable movement through time. The representation of dance steps adopted is a concatenation of vectorized matrices involving the 3D coordinates of tracked body joints. Under this modeling context, a Salsa dance performance is seen as an ordered sequence of Salsa dance steps, requiring a multiple of the variables allocated in the representation of a single step. Following a previous work by Masurelle & Essid that discusses the classification of six Salsa dance steps from 3DLife, we show that it is possible to obtain better classifiers under a similar experimental protocol in terms of both test accuracy and F-measure. By carefully re-annotating the data in 3DLife, we refocus on the six-step classification problem and then extend the protocol to the case of 20 dance steps. In comparison to common classifiers of the trade operating on full-dimensions, we show that it is possible to produce more accurate models by computing a subspace of the data. At the same time it is possible to reduce problematic bias in resulting models due to the uneven distribution of samples across step data classes. We provide and discuss experimental findings to support both hypotheses for the two experimental settings.
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