Effect of treatment with a full-occlusion biofeedback splint on sleep bruxism and TMD pain: a randomized controlled clinical trial

Objectives The purpose of the present study was to analyze treatment outcome with a full-occlusion biofeedback (BFB) splint on sleep bruxism (SB) and TMD pain compared with treatment with an adjusted occlusal splint (AOS). Materials and methods Forty-one patients were randomly allocated to a test (BFB) or a control (AOS) group and monitored over a 3-month period. Output variables were frequency and duration of bruxing events (bursts) and various pain symptoms. Results The BFB group showed a statistically significant reduction in the frequency and duration of bursts and a statistically significant improvement in the patients’ global well-being and the facial muscle pain parameter. After the treatment was stopped, the BFB group showed a statistically significant reduction in the average and maximum duration but no statistically significant change in the frequency of bursts. Conclusions The tested BFB splint is highly effective in reducing SB at the subconscious level, i.e., without waking the patient, and in achieving improvements in global pain perception. The results suggest that the BFB splint also provides a better treatment option for bruxism-related pain than an AOS. However, further research is needed, and specifically studies with a larger patient population displaying higher levels of pain at baseline. Clinical relevance By reducing burst duration and therefore the pathological load on the masticatory apparatus, the BFB splint reduces TMD and bruxism-related symptoms and improves patients’ physical well-being. In the long term, this could prevent damage to the TMJ. This study confirms the effectiveness and safety of this splint. The universal trial number U1111-1239-2450 DRKS-ID registration DRKS00018092

Real-Time Inland CCTV Ship Tracking

The predator algorithm is a representative pioneering work that achieves state-of-the-art performance on several popular visual tracking benchmarks and with great success when commercially applied to real-time face tracking in long-term unconstrained videos. However, there are two major drawbacks of predator algorithm when applied to inland CCTV (closed-circuit television) ship tracking. First, the LK short-term tracker within predator algorithm easily tends to drift if the target ship suffers partial or even full occlusion, mainly because the corner-points-like features employed by LK tracker are very sensitive to occlusion appearance change. Second, the cascaded detector within the predator algorithm searches for candidate objects in a predefined scale set, usually including 3-5 elements, which hampers the tracker to adapt to the potential diverse scale variations of the target ship. In this paper, we design a random projection based short-term tracker which can dramatically ease the tracking drift when the ship is under occlusion. Furthermore, a forward-backward feedback mechanism is proposed to estimate the scale variation between two consecutive frames. We prove that these two strategies gain significant improvements over the predator algorithm and also show that the proposed method outperforms several other state-of-the-art trackers.

Robust Inland Waterway Ship Tracking via Hybrid TLD and Kalman Filter

In this paper, we mainly address the problem of tracking a single ship in inland waterway CCTV (Closed-Circuit Television) video sequences. Although state-of-the-art performance has been demonstrated in TLD (Tracking-Learning-Detection) visual tracking, it is still challenging to perform long-term robust ship tracking due to factors such as cluttered background, scale change, partial or full occlusion and so forth. In this work, we focus on tracking a single ship when it suffers occlusion. To accomplish this goal, an effective Kalman filter is adopted to construct a novel online model to adapt to the rapid ship appearance change caused by occlusion. Experimental results on numerous inland waterway CCTV video sequences demonstrate that the proposed algorithm outperforms the original one.

Building a Multiple Object Tracking System with Occlusion Handling in Surveillance Videos

Video tracking systems are increasingly used day in and day out in various applications such as surveillance, security, monitoring, and robotic vision. In this chapter, the authors propose a novel multiple objects tracking system in video sequences that deals with occlusion issues. The proposed system is composed of two components: An improved KLT tracker, and a Kalman filter. The improved KLT tracker uses the basic KLT tracker and an appearance model to track objects from one frame to another and deal with partial occlusion. In partial occlusion, the appearance model (e.g., a RGB color histogram) is used to determine an object’s KLT features, and the authors use these features for accurate and robust tracking. In full occlusion, a Kalman filter is used to predict the object’s new location and connect the trajectory parts. The system is evaluated on different videos and compared with a common tracking system.

Building a Multiple Object Tracking System with Occlusion Handling in Surveillance Videos

Video tracking systems are increasingly used day in and day out in various applications such as surveillance, security, monitoring, and robotic vision. In this chapter, the authors propose a novel multiple objects tracking system in video sequences that deals with occlusion issues. The proposed system is composed of two components: An improved KLT tracker, and a Kalman filter. The improved KLT tracker uses the basic KLT tracker and an appearance model to track objects from one frame to another and deal with partial occlusion. In partial occlusion, the appearance model (e.g., a RGB color histogram) is used to determine an object’s KLT features, and the authors use these features for accurate and robust tracking. In full occlusion, a Kalman filter is used to predict the object’s new location and connect the trajectory parts. The system is evaluated on different videos and compared with a common tracking system.