To address the issues of false detection and missed detection caused by severe object occlusion and the lack of consideration of relationships among multiple views in the existing multi-view pedestrian detection algorithms， an improved multi-view pedestrian detection algorithm based on MVDeTr （MultiView Detection with shadow Transformer） algorithm was proposed. Firstly， during the feature extraction phase， a view enhancement module — VEM （View Enhancement Module） was designed to enhance important views by focusing on relationships among different views. Secondly， in the process of introducing multi-view information into a single view， an Efficient Multi-scale Attention （EMA） module was added to establish short-distance and long-distance dependencies， thereby improving the detection performance. Finally， based on the Shadow Transformer module in the original baseline algorithm， a new multi-view information processing module — EST （Efficient Shadow Transformer） was designed to reduce the use of redundant information in multiple views while maintaining detection effect. Experimental results show that the proposed algorithm enhances the main detection metric MODA （Multiple Object Detection Accuracy） by 1.8 percentage points， the detection metric MODP （Multiple Object Detection Precision） by 0.6 percentage points， and Recall by 1.8 percentage points on Wildtrack dataset compared to the original MVDeTr algorithm， demonstrating the effectiveness of the proposed algorithm in multi-view pedestrian detection tasks.