Robust Human Detection Using Histogram Oriented Gradient and Aggregate Channel Features

with the development of camera imaging techniques, object detection studies are becoming popular for unmanned aerial vehicles and autonomous systems. However, in these systems, it is extremely important to be able to identify objects effectively and with minimum error in detection for tracking. In this study, an effective human detection system is proposed based on the use of support vector machine, histogram oriented gradient features, and aggregate channel features with AdaBoost classifier. In this proposed system, an adaptive attention system based on the convolutional neural network GoogleNet architecture is developed. Hence efficiency in human detection and monitoring is increased and the central processing unit works faster. Using different data (UAV123, UAV123@10fps, COCO, OpenImagesV6) the most efficient performance is obtained with 97.4% accuracy. In addition, up to 75% savings are achieved with efficient use of the central processing unit. The model in this study is a suitable model for unmanned aerial vehicles, autonomous systems that carry out search and rescue activities, especially close-range target tracking in defense systems.