Lightweight Deep Neural Network Embedded with Stochastic Variational Inference Loss Function for Fast Detection of Human Postures

Feng Shuo Hsu, Zi Jun Su, Yamin Kao, Sen Wei Tsai, Ying Chao Lin, Po Hsun Tu, Cihun Siyong Alex Gong, Chien Chang Chen

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Fusing object detection techniques and stochastic variational inference, we proposed a new scheme for lightweight neural network models, which could simultaneously reduce model sizes and raise the inference speed. This technique was then applied in fast human posture identification. The integer-arithmetic-only algorithm and the feature pyramid network were adopted to reduce the computational complexity in training and to capture features of small objects, respectively. Features of sequential human motion frames (i.e., the centroid coordinates of bounding boxes) were extracted by the self-attention mechanism. With the techniques of Bayesian neural network and stochastic variational inference, human postures could be promptly classified by fast resolving of the Gaussian mixture model for human posture classification. The model took instant centroid features as inputs and indicated possible human postures in the probabilistic maps. Our model had better overall performance than the baseline model ResNet in mean average precision (32.5 vs. 34.6), inference speed (27 vs. 48 milliseconds), and model size (46.2 vs. 227.8 MB). The model could also alert a suspected human falling event about 0.66 s in advance.

Original languageEnglish
Article number336
JournalEntropy
Volume25
Issue number2
DOIs
StatePublished - Feb 2023

Keywords

  • bayesian neural networks
  • gaussian mixture model
  • human posture identification
  • integer-arithmetic-only
  • lightweight neural networks
  • self-attention
  • stochastic variational inference

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