Heterogeneous reconfigurable system for adaptive particle filters in real-time applications

This paper presents a heterogeneous reconfigurable system for real-time applications applying particle filters. The system consists of an FPGA and a multi-threaded CPU. We propose a method to adapt the number of particles dynamically and utilise the run-time reconfigurability of the FPGA for reduced power and energy consumption. An application is developed which involves simultaneous mobile robot localisation and people tracking. It shows that the proposed adaptive particle filter can reduce up to 99% of computation time. Using run-time reconfiguration, we achieve 34% reduction in idle power and save 26-34% of system energy. Our proposed system is up to 7.39 times faster and 3.65 times more energy efficient than the Intel Xeon X5650 CPU with 12 threads, and 1.3 times faster and 2.13 times more energy efficient than an NVIDIA Tesla C2070 GPU. © 2013 Springer-Verlag.

Real time feature-based facial tracking using Lie algebras

We have developed a novel human facial tracking system that operates in real time at a video frame rate without needing any special hardware. The approach is based on the use of Lie algebra, and uses three-dimensional feature points on the targeted human face. It is assumed that the roughly estimated facial model (relative coordinates of the three-dimensional feature points) is known. First, the initial feature positions of the face are determined using a model fitting technique. Then, the tracking is operated by the following sequence: (1) capture the new video frame and render feature points to the image plane; (2) search for new positions of the feature points on the image plane; (3) get the Euclidean matrix from the moving vector and the three-dimensional information for the points; and (4) rotate and translate the feature points by using the Euclidean matrix, and render the new points on the image plane. The key algorithm of this tracker is to estimate the Euclidean matrix by using a least square technique based on Lie algebra. The resulting tracker performed very well on the task of tracking a human face.

Real-time experimental demonstration of low-cost VCSEL intensity-modulated 11.25 Gb/s optical OFDM signal transmission over 25 km PON systems.

The feasibility of utilising low-cost, un-cooled vertical cavity surface-emitting lasers (VCSELs) as intensity modulators in real-time optical OFDM (OOFDM) transceivers is experimentally explored, for the first time, in terms of achievable signal bit rates, physical mechanisms limiting the transceiver performance and performance robustness. End-to-end real-time transmission of 11.25 Gb/s 64-QAM-encoded OOFDM signals over simple intensity modulation and direct detection, 25 km SSMF PON systems is experimentally demonstrated with a power penalty of 0.5 dB. The low extinction ratio of the VCSEL intensity-modulated OOFDM signal is identified to be the dominant factor determining the maximum obtainable transmission performance. Experimental investigations indicate that, in addition to the enhanced transceiver performance, adaptive power loading can also significantly improve the system performance robustness to variations in VCSEL operating conditions. As a direct result, the aforementioned capacity versus reach performance is still retained over a wide VCSEL bias (driving) current (voltage) range of 4.5 mA to 9 mA (275 mVpp to 320 mVpp). This work is of great value as it demonstrates the possibility of future mass production of cost-effective OOFDM transceivers for PON applications.

Real-time block flow tracking of atrial septal defect motion in 4D cardiac ultrasound

Real-time cardiac ultrasound allows monitoring the heart motion during intracardiac beating heart procedures. Our application assists atrial septal defect (ASD) closure techniques using real-time 3D ultrasound guidance. One major image processing challenge is the processing of information at high frame rate. We present an optimized block flow technique, which combines the probability-based velocity computation for an entire block with template matching. We propose adapted similarity constraints both from frame to frame, to conserve energy, and globally, to minimize errors. We show tracking results on eight in-vivo 4D datasets acquired from porcine beating-heart procedures. Computing velocity at the block level with an optimized scheme, our technique tracks ASD motion at 41 frames/s. We analyze the errors of motion estimation and retrieve the cardiac cycle in ungated images. © 2007 IEEE.

Real-time marker prediction and CoR estimation in optical motion capture

Optical motion capture systems suffer from marker occlusions resulting in loss of useful information. This paper addresses the problem of real-time joint localisation of legged skeletons in the presence of such missing data. The data is assumed to be labelled 3d marker positions from a motion capture system. An integrated framework is presented which predicts the occluded marker positions using a Variable Turn Model within an Unscented Kalman filter. Inferred information from neighbouring markers is used as observation states; these constraints are efficient, simple, and real-time implementable. This work also takes advantage of the common case that missing markers are still visible to a single camera, by combining predictions with under-determined positions, resulting in more accurate predictions. An Inverse Kinematics technique is then applied ensuring that the bone lengths remain constant over time; the system can thereby maintain a continuous data-flow. The marker and Centre of Rotation (CoR) positions can be calculated with high accuracy even in cases where markers are occluded for a long period of time. Our methodology is tested against some of the most popular methods for marker prediction and the results confirm that our approach outperforms these methods in estimating both marker and CoR positions. © 2012 Springer-Verlag.