Aiming at the problem that the existing attitude estimation method cannot accurately extract the feature parameters, this paper proposed a hierarchical model based on structured support vector machine(SSVM) and convolutional neural network(CNN). Firstly, it showed how a SSVM based on the PS component model could be implemented as a two-layer neural network, where the first layer was the convolutional layer and the other layer was the loss-enhanced inference layer. Then, by transforming the structured form of the model into a neural network in the model, the proposed method could simultaneously learn the structural model and the appearance model, and then backpropagated the error to learn the underlying learnable parameters. These parameters could be extracted from the appearance model features. Finally, it transformed the SSVM model into a neural network model, and propagated the error back to the lower layer, and calculated the exact SSVM loss, while learnt the original SSVM by the sub-gradient-based method. Comparing the model with the current advanced recognition model, the results show that the proposed success rate of the hierarchical model is 6% higher than the comparison method and has stronger recognition performance.

[2020-05-05] Printed Article /doc/2018.11.0855/v2