No abstract

No abstract

Since the Viola and Jones' method on real-time face detection was proposed in 2001, numerous works for object detection, person recognition, and object tracking have been published by papers and journals. Each method has its strong points and drawbacks. That means that in a system which only employs a standalone method, we could only get either speed or accuracy. In this paper, we proposed a state-machine method to combine face recognition, face detection, and tracker to harness the tracker promptness while maintaining the ability to distinguish the person of interest with the other person and backgrounds, to overcome the limitations of the standalone method. Subsequently, the information gathered from this image processing side will be delivered to the hardware tracker. The image processing side becomes a visual sensor that provides feedback or measurement value i.e. center point coordinate value from the detected face. The 2 DOF hardware tracker camera platform being used implements Model Predictive Control to calculate required control action thus the platform is able to track the target object, keeping it at the center of the frame. MPC method is chosen because it produces an optimal control signal while considering the input signal saturation aspect. The MPC control signals deliver a good control pan and tilt system response with rise time < 1 second and overshoot <15%. It is also noticed that the FSM implemented in this paper is able to meet the goal with a considerable performance for indoor settings.

COVID-19 outbreak has a big impact to people's daily life in 2020, especially in healthcare sector. As COVID-19 viruses are highly contagious, it is important to take strict measures to ensure all patients got the needed care while taking healthcare workers safety into consideration. Robot-based care is being hurriedly developed recently, and one of the important abilities for such robot is to be able to distinguish object commonly found in hospital thus the robot can make the correct action towards the correct object. For this publication, an object detector is trained to detect the hospital bed, thus it can be an input to the care robot navigation system when it is going to approach patients. As hospital beds vary from one brand to another, and this research has limited time constraint and readily available hardware, the object detector confidence is still low. Thus, a centroid tracking method is implemented to aid the hospital object detection, ensuring the robot can detect the correct bed more robustly with considerable speed for embedded implementation.

No abstract