Crowd counting is one of the most challenging issues in computer vision community for safety and security through surveillance systems. It has extensive range of applications, such as disaster management, surveillance event detection, intelligence gathering and analysis, public safety control, trafﬁc monitoring, design of public spaces, anomaly detection and military. Early approaches still encounter many issues, like non-uniform density distribution, partial occlusion and discrepancies in scale and point of view. To address the above problems, Feature Pyramid Networks are introduced in deep convolution networks for counting the individuals in the Crowd. The designed network has extracted the features at all resolutions and is constructed rapidly from only one input image. This method achieves out-performance results compared to the well-known networks on three demanding standard crowd counting datasets.