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Designing an adaptive cost function for dynamic human pose predictions
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DOI：10.1007/s11042-023-17736-1.png)
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In the modern-day scenario, machines and humans are expected to work together and collaborate in several social and manufacturing environments. The machines should predict humans' next move for effective collaborations by observing their present move. Human motion modelling and prediction are fundamental and challenging problems involving computer vision and graphics. To help solve some of the challenges, in the present investigation, we propose an innovative idea of developing a new cost function as the objective function based on adaptive sampling, which is subsequently used with an 'Adam' optimizer for training and validating a specially configured Deep Learning architecture. Our proposed development produced significantly improved results regarding future pose estimation/predictions. The adaptiveness of the proposed cost function is based on a bell-shaped locally weighted function. It has been observed that the area covered by the cost function plays a vital role during training, and the bell-shaped function's width helps decide the region of importance for the training samples. The proposed cost function has been used for training a gated recurrent unit (GRU) based encoder-decoder architecture. The encoder takes the observed input sequences, extracts the input sequence's significant variability, and passes it to the decoder. The decoder takes it as input, trains using the adaptive sampling-based method, and predicts future poses. We have experimented with this function in various sizes and shapes and compared the results obtained with some state-of-the-art research results. As elaborated in this paper, we obtained much-improved results in almost all the cases.
Keyword:
Deep sequential networks
Encoder-decoder
Human 3.6M
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论文数:
2.0W
被引数:
3.2W
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