This paper introduces a novel approach to balance the accuracy and efficiency in Convolutional Neural Networks (CNNs). The main contribution is a dual strategy that exploits feature reuse along a squeezed architecture. The idea is to integrate fire units from the SqueezeNet architecture, replacing standard convolutions to reduce the number of trainable parameters, and introducing data reuse through additional bypass connections to maintain accuracy. While the idea can be broadly applicable, we demonstrate its effectiveness by applying it to the UNet architecture, leading to the development of a novel network called Squeeze-Bypass-UNet (SBp-UNet). The network is therefore used for the challenging task of pedestrian semantic segmentation, particularly in scenarios involving heavy occlusions. We validate our model on the Occluded Human (OCHuman) dataset, demonstrating superior performance to the original UNet with 9.33 times fewer parameters. Further validation on the Pascal VOC dataset confirms the robust predictive ability of SBp-UNet. Our work thus offers a novel solution for the efficiency-accuracy problem in CNNs, with proven application in pedestrian segmentation.