Stacked hourglass network has become an important model for Human pose estimation. The estimation of human body posture depends on the global information of the keypoints type and the local information of the keypoints location. The consistent processing of inputs and constraints makes it difficult to form differentiated and determined collaboration mechanisms for each stacked hourglass network. In this paper, we propose a Multi-Scale Stacked Hourglass (MSSH) network to high-light the differentiation capabilities of each Hourglass network for human pose estimation.   The pre-processing network forms feature maps of different scales,and dispatch them to various locations of the stack hourglass network, where the small-scale features reach the front of stacked hourglass network, and large-scale features reach the rear of stacked hourglass network.    And a new loss function is proposed for multi-scale stacked hourglass network.   Different keypoints have different weight coefficients of loss function at different scales, and the keypoints weight coefficients are dynamically adjusted from the top-level hourglass network to the bottom-level hourglass network.   Experimental results show that the pro-posed method is competitive with respect to the comparison algorithm on MPII and LSP datasets. Human pose estimation need locate the body keypoints (head, shoulder, elbow, wrist, knee, ankle, etc.) from the input image, and it is basic method for some advanced vision task BID4 , such as human motion recognition, human-computer interaction, and human reidentification et al. We focus on single-person pose estimation problems in a single RGB image. Due to the high flexibility of the human body and limbs, diverse viewpoint, camera projection transformation and occlusion, it still is a difficult task to accurately determine body keypoints from a single image.In recent years, the deep convolutional neural network (DCNN) has made significant progress in the human pose estimation; especially the stacked hourglass network BID7 has achieved good results and has attracted much attention. The human pose estimation involves two kinds of information: the type and location of body keypoints. The type of body keypoints needs to be determined in a larger receptive field, and the location of body keypoints needs to be based on the specific pixel position, which are respectively equivalent to global information and local information. The hourglass network uses a convolution layer and a deconvolution layer to form an hourglass structure, and establish crossover channels between convolution and deconvolution layers on different scales. Using the hourglass network for human pose estimation, the hourglass structure extracts global information through information compression, and the crossover channels compensates for local information loss in information compression. The stacked hourglass network continuously improves the human pose estimation by enhancing the context constraints among body keypoints through the stacked structure.The stacked hourglass network theoretically increases the stacked depth to expand the receptive field and form a stronger context constraints among body keypoints. However, in practical applications, simply increasing the stacked depth is difficult to effectively improve the accuracy of human pose estimation. The main reason is that the consistent processing of inputs and constraints makes it difficult to form differentiated and determined collaboration mechanisms for each stacked hourglass network, to make up the information loss caused by the functional consistency of hourglass networks. Inspired by multi-scale information fusion from the single hourglass network, we propose a Multi-Scale Stacked Hourglass (MSSH) network. A pre-processing network consisting primarily of residual networks is designed to generate multi-scale features, and features of each scale are sent to different stacked hourglass networks. Each hourglass network has different inputs: the output of the pre-level hourglass network, and the received feature. Small-scale feature input makes the hourglass network tend to focus on global information, such as the type and context constraints of body keypoints, and large-scale feature input makes the hourglass network more likely to focus on local information, such as the location of body keypoints. The input of entire stacking hourglass network has changed from small-scale feature to large-scale feature, and it is a top-down route of body keypoints estimation. The iterative relationship between the loss functions of different scales of the MSSH network is established, so that the pre-level detection results affect the weight of the next keypoint loss function, and the optimization process of network model training is controlled by adaptive weighted loss function. The iterative relationship of the adjacent network loss function is established on the MSSH network, so that the weight of the loss function on pre-level hourglass network affects the weight of the loss function on the current hourglass network. The optimization process of the model training is controlled by the adaptive weighted loss function.The main contributions of this paper can be summarized as follows:• In a multi-scale stacked hourglass network, the pre-processing network generates features of different scales and dispatchs them to every hourglass network, where the small-scale features reaches the front of stacked hourglass network and the large-scale features reaches the rear of stacked hourglass network. From global information to local information, each hourglass network can form a differentiated function, which is conducive to the formation of collaborative processing.• A new loss function of the MSSH network is proposed. The weighting coefficient of the loss function in hourglass network is defined. The weight coefficient and the loss function of the pre-level hourglass network are used to adjust the weight coefficient of the current hourglass network, and the convergence process of the model training is optimized by the adaptive weighted loss function.The remainder of this paper is organized as follows. Section 2 briefly reviews recent work on human pose estimation. Section 3 details the structure of the MSSH network. Section 4 describes the new loss function for MSSH network networks. Section 5 describes the implementation details and experimental results. Section 6 summarizes our paper.
Differentiated inputs cause functional differentiation of the network, and the interaction of loss functions between networks can affect the optimization process. A modification to the original hourglass network for single pose estimation that yields improvements over the original baseline. Authors extend a stacked hourglass network with inception-resnet-A modules and propose a multi-scale approach for human pose estimation in still RGB images.