Study on Tracking Real-Time Target Human Using Deep Learning for High Accuracy

Abstract

Speed and accuracy are important parts of the human tracking system. To design a system that tracks the target human working well in real time, as well as on mobile devices, a tracking real-time target human system is proposed. First, real-time human detection is performed by the combination of MobileNet-v2 and single-shot multibox detector (SSD). Subsequently, the particle filter algorithm is applied to track the target human. The proposed system is evaluated with the different color shirts and complex background conditions. In addition, the system also works with the support of a depth Kinect-v2 camera to evaluate performance. The experiment result indicates that the proposed system is efficient without the impact of colors, background, and light. Moreover, the system still tracks the human when the human has disappeared or the size of the target has a significant change, and an FPS of 12 (Kinect-v2 camera) and 22 (conventional camera) ensures the system works well in real time.