Real‐time running detection system for UAV imagery based on optical flow and deep convolutional networks

Abstract

A fast-running human detection system for the unmanned aerial vehicle (UAV) based on optical flow and deep convolution networks is proposed in this study. In the system, running humans can be detected in real-time at the speed of 15 frames per second (fps) with an 81.1% detection accuracy. To fast locate the candidate targets, optical flow representing the motion information is calculated with every two successive frames. A series of prior-processing operations, including spatial average filtering, morphological expansion and outer contour extraction, are performed to extract the regions of interest. A classification model based on small-kernel convolution networks is proposed to achieve the accurate recognition of the running people in various backgrounds. In the model, small convolutional filters are adopted to accelerate the speed of the data representation. Moreover, a total of 60,000 samples are collected to enhance the robustness of the model to adapt to the complex outdoor UAV scenes. The proposed method is compared with other deep learning frameworks for object detection. Field experiments on UAV videos are performed to verify that the proposed system can effectively detect the running people targets in real-time.

Vision‐based tracking control of quadrotor for a moving target with nonlinear angular velocity observer

“…Quadrotor unmanned aerial vehicles (UAVs), with the characteristics of vertical takeoff and landing and high maneuverability, have received a growing interest during the last 10 years. Due to its huge potential, UAV system is widely applied and investigated in business and academic field, such as state estimation [1], surveillance [2], target tracking [3,4], optical flow-based landing and localization [5], and bridge inspection 3D mapping and swarm missions [6][7][8]. Nowadays, more and more vision-based control tasks of UAV system are proposed, where the tracking problems are special difficult, such as pursuing fleeing vehicle, observing animal migration, and aerial photographing in sports.…”

Pedestrian Motion Path Detection Method Based on Deep Learning and Foreground Detection

“…is article believes that the coincidence of the two detection boundary frames should be as high as possible. However, through a large number of experiments and theoretical analysis, it can be seen that because the background difference method needs to expand the binary image, the low degree of expansion processing may lead to incomplete pedestrian area, and the high degree of processing often leads to the outer rectangular frame to be larger than the actual pedestrian area [19][20][21][22]. Firstly, the coordinates, width, and height of the two kinds of bounding boxes are compared.…”

Design and Implementation of Intelligent Inspection and Alarm Flight System for Epidemic Prevention

“…If UAVs can be used to estimate crowd density and detect the wearing of masks, they can save human resources, and also analyze, prevent, and deal with emergencies effectively. At present, the commonly used target detection methods of UAVs include the optical flow method [12], the matching method, the background difference method [13], and the frame difference method [14], as well as the method based on deep convolutional neural networks, which has performed well in recent years. The optical flow method can achieve better target detection when the camera is moving.…”