The role of robot following is crucial for effective human-robot collaboration. Traditional methods often rely on maintaining a significant distance between the robot and the human, which limits interaction and responsiveness. In contrast, close-proximity front-following facilitates immediate engagement, enhancing user experience and improving human-robot interaction. Nonetheless, it presents challenges in accurately interpreting human walking intentions due to a restricted observational field. In our paper, we introduce an innovative Depth-Temporal Attention Network that takes lower-limb depth images and robot motor signals as input, to accurately predict human walking intentions. This network leverages a depth attention module to capture essential spatial features and integrates a temporal attention mechanism to analyze movement dynamics. To enhance generalization, we use a domain adversarial module that focuses on shared features across diverse walking data, ensuring consistent performance across users. Experimental results demonstrate that our approach achieves an impressive average intention prediction accuracy of 91.09%, significantly surpassing baseline models by 12.59% to 23.66%. Additionally, an ablation study reveals that the depth-attention module substantially improves the model's understanding of depth features, resulting in an 11.44% increase in accuracy. With this high prediction accuracy, smooth front-following is achieved at close-proximity.