  Accurate pedestrian counting algorithm is critical to eliminate insecurity in
the congested public scenes. However, counting pedestrians in crowded scenes
often suffer from severe perspective distortion. In this paper, basing on the
straight-line double region pedestrian counting method, we propose a dynamic
region division algorithm to keep the completeness of counting objects.
Utilizing the object bounding boxes obtained by YoloV3 and expectation division
line of the scene, the boundary for nearby region and distant one is generated
under the premise of retaining whole head. Ulteriorly, appropriate learning
models are applied to count pedestrians in each obtained region. In the distant
region, a novel inception dilated convolutional neural network is proposed to
solve the problem of choosing dilation rate. In the nearby region, YoloV3 is
used for detecting the pedestrian in multi-scale. Accordingly, the total number
of pedestrians in each frame is obtained by fusing the result in nearby and
distant regions. A typical subway pedestrian video dataset is chosen to conduct
experiment in this paper. The result demonstrate that proposed algorithm is
superior to existing machine learning based methods in general performance.
