Abstract:

Human detection is of great significance in computer vision tasks such as security and human-machine interaction. In this paper, a high-precision detection model based on the indoor human detection dataset(IHDD) is proposed for indoor human detection. As the posture of the indoor staff is changeable and the image shooting angle is quite different from that of outdoor pedestrians, the model we propose makes significant improvement in the field of human detection. In this work, we integrate the Squeeze-and-Excitation module into the residual network to realize the dropout of the convolutional layer to enhance the generalization ability of the model. Meanwhile, dimension clustering is utilized to find the optimal size of anchors and the best feature map to be used in prediction. Experimental results on IHDD demonstrate that the proposed methods can reach a precision of 99.84% and outperform other algorithms in terms of speed and memory usage. It indicates that our method has a certain theoretical and practical value.

Key words: machine vision, convolutional neural nets, indoor human detection