Intelligent Visual Tracking in Unstabilized Videos

Visual tracking requirement is increasing day by day due to the availability of high-performance digital cameras at low prices. Visual tracking becomes a complex problem when cameras suffer with unwanted and unintentional motion, resulting in motion-blurred unstabilized video. The problem in hand becomes more challenging when the target of interest is to be detected automatically in this unstabilized video. This paper presents a comprehensive single intelligent solution for these problems. The proposed algorithm auto-detects the camera motion, filters out the unintentional motion while stabilizing the video keeping intentional motion only using speeded-up robust features (SURF) technique. Motion smear due to unstabilization is also removed, providing sharp stabilized video output with video quality enhancement of up to 20dB. Gabor filter is used innovatively for auto-detection of target of interest in each stabilized frame. Then the target is tracked using SURF method.

A Walking-in-Place Method for Virtual Reality Using Position and Orientation Tracking

People are interested in traveling in an infinite virtual environment, but no standard navigation method exists yet in Virtual Reality (VR). The Walking-In-Place (WIP) technique is a navigation method that simulates movement to enable immersive travel with less simulator sickness in VR. However, attaching the sensor to the body is troublesome. A previously introduced method that performed WIP using an Inertial Measurement Unit (IMU) helped address this problem. That method does not require placement of additional sensors on the body. That study proved, through evaluation, the acceptable performance of WIP. However, this method has limitations, including a high step-recognition rate when the user does various body motions within the tracking area. Previous works also did not evaluate WIP step recognition accuracy. In this paper, we propose a novel WIP method using position and orientation tracking, which are provided in the most PC-based VR HMDs. Our method also does not require additional sensors on the body and is more stable than the IMU-based method for non-WIP motions. We evaluated our method with nine subjects and found that the WIP step accuracy was 99.32% regardless of head tilt, and the error rate was 0% for squat motion, which is a motion prone to error. We distinguish jog-in-place as “intentional motion” and others as “unintentional motion”. This shows that our method correctly recognizes only jog-in-place. We also apply the saw-tooth function virtual velocity to our method in a mathematical way. Natural navigation is possible when the virtual velocity approach is applied to the WIP method. Our method is useful for various applications which requires jogging.

Theoretical Development and Experimental Validation of an Adaptive Controller for Tremor Suppression at Musculoskeletal Level

Tremor is a rhythmical and involuntary oscillatory movement of a body part. Mechanical loading via wearable exoskeletons is a non-invasive tremor suppression alternative to medical treatments. In this approach, the challenge is attenuating the tremor without affecting the patient’s intentional motion. An adaptive tremor suppression algorithm was designed to estimate and restrict motion within the tremor frequency band. An experimental setup was designed and developed to simulate the dynamics of a human arm joint with intentional and tremorous motion. The required orthotic suppressive force was applied via a pneumatic cylinder. The algorithm was implemented with a real-time controller and experimental results show tracking of the tremor frequency and a 97% reduction of tremor amplitude at the fundamental frequency.