Abstract
Most semantic segmentation models based on deep convolutional neural network (CNN) typically require a large number of weight parameters, high hardware resources for storage and computation. Moreover, redesigning a compact network suffers from some training problems, such as under-fitting. A human segmentation algorithm is proposed based on compressed deep CNN to optimize the convolution layers and filters. PSPNet-50 is fine-tuned on the human segmentation dataset to obtain the human segmentation model with higher accuracy. Then the convolutional-layer level pruning and corresponding structure optimization are performed so that the parameters of the model are substantially reduced. Finally, the two-stage global filter-level pruning strategy is used. Compared with the method of layer by layer pruning and retraining, our strategy not only reduces parameters of the model and saves the time of retraining, but also keeps the high IoU (Intersection over Union) accuracy. In addition, by adding auxiliary losses in the network during training CNN, the supervised training of the network is improved, and IoU is further increased. Compared to the model before compression, the sufficient experiments show that the parameter number, computation cost, memory consumption, and parameter storage are decreased by 1/7.5, 5.6/6.6, 0.7/1, 6.5/7.5, respectively, while the segmentation speed is accelerated by 2.4 times, and IoU on test set reaches 93.2%.
Highlights
Human segmentation refers to segmenting human regions from the background
With the rapid development of deep learning, many semantic segmentation methods based on deep convolutional neural network (CNN) have emerged, such as FCN [10], DeepLab [11], PSPNet [1], MDCCNets [12], etc., which can automatically learn the deep features of object representation, and yield better results than the traditional segmentation methods
We assign the number of output feature maps of the last convolutional layer in PSPNet-50 [1] to 2, which correspond to the background and foreground, respectively, and use the training set to fine-tune the human segmentation model
Summary
Human segmentation refers to segmenting human regions from the background. As the foundation of analysis and understanding of human behavior, segmentation results are important to the subsequent works, such as 3D reconstruction, recognition, detection, and tracking. The models trained with these segmentation algorithms can be used for portrait images; it is difficult to obtain high IoU because they are not trained for human body images. J. Miao et al.: Human Segmentation Based on Compressed Deep CNN of computation and storage are high. A human segmentation algorithm is proposed based on a compressed deep CNN, which reduces the resources for computation and storage while yields high IoU. The convolutional layers and filters that have little impact on accuracy are pruned, so the amount of parameters and calculation is reduced while preserving high IoU. The sufficient experiments on the human segmentation dataset demonstrate that our algorithm outperforms the commonly used models in terms of model size, segmentation speed and accuracy.
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