An indoor human detection dataset (IHDD) is established, and a novel multi-view indoor human detection neural network (MVNN) based on joint learning is proposed. The model consists of input data layer, feature extraction layer, deformation layer, visibility reasoning layer and classification layer, and the proposed MVNN algorithm can improve the detection performance when combined with the region proposal model and the multi-view model. Experimental results on the self-built IHDD show that compared with other existing detection algorithms, the proposed MVNN algorithm has a higher detection rate. It can still obtain good detection results even in the case of difficult situations such as various views, changing poses and occlusion for human targets, which indicates certain theoretical research value and practical value.