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Rapid human motion pose tracking has extensive applications in fields such as motion capture, intelligent monitoring, sports training, and physical health management. It can provide accurate data support, enhance safety monitoring, optimize training outcomes, and promote physical health. Traditional human pose tracking methods predominantly rely on either sensors or images for tracking, which often results in issues like low tracking accuracy and slow tracking speed. To address these problems, a rapid human motion pose tracking method based on improved deep reinforcement learning and multimodal fusion is proposed. First, this paper designs an overall architecture for rapid human motion pose tracking and utilizes a combination of monocular vision and sensors to extract and collect human motion data. Second, it constructs a complementary filter-based multimodal data fusion method to merge the multimodal data and extract the fused features. Finally, a multi-level attention network is employed to enhance the deep reinforcement learning network, using the fused features as input for training to achieve rapid human motion pose tracking. The results show that the proposed method can achieve efficient and stable human motion pose tracking in complex scenes, with a tracking accuracy of up to 85% and a shortest tracking time of 72ms, which has practical application value.

Nowadays, mobile robots are applied in a variety of fields to assist humans in doing complicated work such as disaster situations, rescue missions, security and tracking systems, military campaigns, and planetary discovery. When performing these tasks, climbing uneven terrain and detecting targets are two of the most important requirements for the mobile robot. The Rocker-Bogie design developed by the NASA Mars Exploration Rover (MER) Project has become a proven mobility application during the last decade. The stability and obstacle-climbing capability of this model are evaluated as suitable choices for mobile robots. In this paper, a prototype of a Rocker-Bogie mobile robot featuring a human tracking system is proposed. To be more detailed, the modified YOLOv8 network is designed to detect humans and corresponds to the operation of the robot in real time. The computer then computes the associated speeds of the robot according to the detection targets. The velocity signal is transmitted back to the robot, which then executes the appropriate maneuver. The experimental result demonstrates an average success rate of 91% for tracking missions on an actual mobile robot platform under different scenarios.

Vision-based recognition of the object as a general interface gives us high cost and complicated problem. This research suggests human tracking system by Arduino, and Laser-CdS cell system track wire that pass laser line. In this paper, we review existing literature on application systems of recognition which involves many interdisciplinary studies. We conclude that our method can only reduce cost, but is easy way to trace people's location with the use of wire. Furthermore, we apply several recognition systems including CdS-based mobile robot that is applied IV stand used at the hospital effectively.