GoatPose: A Lightweight and Efficient Network with Attention Mechanism

Keypoint detection is an essential part of human pose estimation. However, due to resource constraints, it’s still a challenge to deploy complex convolutional networks to edge devices. In this paper, we present GoatPose: a lightweight deep convolutional model for real-time human keypoint detection incorporating attention mechanism. Since the high computational cost is associated with the frequently-use convolution block, we substitute it with our new-designed LiteConv block, which conducts cheap linear operation to generate rich feature maps from the intrinsic features with low cost. This method significantly accelerates the model while inevitably losing a part of spatial information. To compensate for the information loss, we introduce NAM attention mechanism. By applying channel weighting, the model can focus more on the important features and enhance the feature representation. Results on the COCO dataset show the superiority of our model. With the complexity of our model reduced by half and the computational speed doubled, the accuracy of our model is basically the same as that of the backbone model. We further deploy our model on NVIDIA Jetson TX2 to validate its real-time performance, indicating that our model is capable of being deployed and widely adopted in real-world scenarios.