Abstract
3D skeletal based action recognition is being practiced with features extracted from joint positional sequence modeling on deep learning frameworks. However, the spatial ordering of skeletal joints during the entire action recognition lifecycle is found to be fixed across datasets and frameworks. Intuition inspired us to investigate through experimentation, the influence of multiple random skeletal joint ordered features on the performance of deep learning systems. Therefore, the argument: can joint order independent learning for skeletal action recognition practicable? If practicable, the goal is to discover how many different types of randomly ordered joint feature representations are sufficient for training deep networks. Implicitly, we further investigated on multiple features and deep networks that recorded highest performance on jumbled joints. This work proposes a novel idea of learning skeletal joint volumetric features on a spectrally graded CNN to achieve joint order independence. Intuitively, we propose 4 joint features called as quad joint volumetric features (QJVF), which are found to offer better spatio temporal relationships between time series joint data when compared to existing features. Consequently, we propose a Spectrally graded Convolutional Neural Network (SgCNN) to characterize spatially divergent features extracted from jumbled skeletal joints. Finally, evaluation of the proposed hypothesis has been experimented on our 3D skeletal action KLHA3D102, KLYOGA3D datasets along with benchmarks, HDM05, CMU and NTU RGB D. The results demonstrated that the joint order independent feature learning is achievable on CNNs trained on quantified spatio temporal feature maps extracted from randomly shuffled skeletal joints from action sequences.
Highlights
T HE Skeletal based action recognition is being practiced through deep learning on features extracted from 3D joint sequences
We propose a 4joint relational map called as quad joint volume feature map (QJVMs) which is elaborated
C1: MONOSKEL RESULTS To begin with, we focus on the performance of our proposed quad joint volume maps (QJVM) and the novel architecture Spectrally Graded convolutional neural networks (CNNs) (SgCNN) as a traditional approach where the skeletal joints are unaltered throughout the experiment, MONOSKEL
Summary
T HE Skeletal based action recognition is being practiced through deep learning on features extracted from 3D joint sequences. The idea behind this skeletal joint random order training on the deep learning networks is to learn different possible random feature representations for a robust action recognition framework. When we the models trained on 3D motion capture skeletal data and used on test inputs from Kinect data with similar number of joints has again resulted in a failed model To convert these data preprocessing anomalies into refined information, there are two methods. 2) To identify the number of randomly ordered joint feature maps required for training the designed SgCNN that results in sovereign 3D skeletal action recognition systems. The results of this study are important for attaining explicit understanding of joint ordering in 3D skeletal based action recognition on deep learning networks.
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