Evolutionary joint selection to improve human action recognition with RGB-D devices

Abstract

Interest in RGB-D devices is increasing due to their low price and the wide range of possible applications that come along. These devices provide a marker-less body pose estimation by means of skeletal data consisting of 3D positions of body joints. These can be further used for pose, gesture or action recognition. In this work, an evolutionary algorithm is used to determine the optimal subset of skeleton joints, taking into account the topological structure of the skeleton, in order to improve the final success rate. The proposed method has been validated using a state-of-the-art RGB action recognition approach, and applying it to the MSR-Action3D dataset. Results show that the proposed algorithm is able to significantly improve the initial recognition rate and to yield similar or better success rates than the state-of-the-art methods.

Fusion of Skeletal and Silhouette-Based Features for Human Action Recognition with RGB-D Devices

“…Each joint is represented by its 3D position in the real world. In order to achieve invariance to scale and rotation, each sequence is normalised following the method in [8]:…”

Human Action Recognition with RGB-D Sensors

“…These parameters can be chosen randomly or using some optimization strategies in order to maximize the performance. In this chapter, results are shown using both the options, adopting the optimization process, based on evolutionary [70] and coevolutionary [71] algorithms. These optimization strategies are applied as wrapper methods, associating the fitness of each individual in the population to the accuracy of the action recognition algorithm.…”

Space-time representation of people based on 3D skeletal data: A review

“…, whereS is the mean of all S. Since not all the joints are equally informative, several methods were proposed to select key joints that are more descriptive [174,175,176,177]. Chaaraoui et al [174] introduced an evolutionary algorithm to select a subset of skeleton joints to form features.…”

“…Given a new instance, this encoding methodology uses the normalized frequency vector of code occurrence as the final feature vector. Bag-of-words encoding is widely employed by a large number of skeleton-based human representations [174,143,144,210,186,109,150,127,188,189,115,152,190,192,159,147,165,167,179,162,218,219]. According to how the dictionary is learned, the encoding methods can be broadly categorized into two groups, based on clustering or sparse coding.…”

“…Chaaraoui et al [174] introduced an evolutionary algorithm to select a subset of skeleton joints to form features. Then a normalizing process was used to achieve position, scale and rotation invariance.…”