Development of Human Motion Prediction Strategy using Inception Residual Block
Shekhar Gupta, Gaurav Kumar Yadav, G. C. Nandi

TL;DR
This paper introduces an Inception Residual Block combined with Graph Convolutional Networks for improved human motion prediction, demonstrating superior accuracy on the Human 3.6M dataset.
Contribution
It proposes a novel architecture integrating inception residual blocks with GCNs for better spatial-temporal feature learning in human motion prediction.
Findings
Achieves higher prediction accuracy than existing methods.
Effectively captures multi-scale temporal features.
Outperforms state-of-the-art models on the Human 3.6M dataset.
Abstract
Human Motion Prediction is a crucial task in computer vision and robotics. It has versatile application potentials such as in the area of human-robot interactions, human action tracking for airport security systems, autonomous car navigation, computer gaming to name a few. However, predicting human motion based on past actions is an extremely challenging task due to the difficulties in detecting spatial and temporal features correctly. To detect temporal features in human poses, we propose an Inception Residual Block(IRB), due to its inherent capability of processing multiple kernels to capture salient features. Here, we propose to use multiple 1-D Convolution Neural Network (CNN) with different kernel sizes and input sequence lengths and concatenate them to get proper embedding. As kernels strides over different receptive fields, they detect smaller and bigger salient features at…
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Taxonomy
MethodsConvolution · Batch Normalization · Residual Connection · *Communicated@Fast*How Do I Communicate to Expedia? · Residual Block
