Target Detection and Tracking With Heterogeneous Sensors

We present a multimodal detection and tracking algorithm for sensors composed of a camera mounted between two microphones. Target localization is performed on color-based change detection in the video modality and on time difference of arrival (TDOA) estimation between the two microphones in the audio modality. The TDOA is computed by multiband generalized cross correlation (GCC) analysis. The estimated directions of arrival are then postprocessed using a Riccati Kalman filter. The visual and audio estimates are finally integrated, at the likelihood level, into a particle filter (PF) that uses a zero-order motion model, and a weighted probabilistic data association (WPDA) scheme. We demonstrate that the Kalman filtering (KF) improves the accuracy of the audio source localization and that the WPDA helps to enhance the tracking performance of sensor fusion in reverberant scenarios. The combination of multiband GCC, KF, and WPDA within the particle filtering framework improves the performance of the algorithm in noisy scenarios. We also show how the proposed audiovisual tracker summarizes the observed scene by generating metadata that can be transmitted to other network nodes instead of transmitting the raw images and can be used for very low bit rate communication. Moreover, the generated metadata can also be used to detect and monitor events of interest.

Envelope-tracking-based Doherty power amplifier

From the instantaneous efficiency plot, it is observed that the conventional 2-stage Doherty power amplifier (DPA) with high upper power dynamic range (>12 dB) suffers from a substantial dip in the middle of the upper power regime, thus reducing the average efficiency. In this study, an envelope-tracking-based DPA is proposed in order to minimise this dip by adjusting the drain bias voltage of the auxiliary amplifier of the DPA proportional to the input power level.

Rao-Blackwellised particle filter for colour-based tracking

Colour-based particle filters have been used exhaustively in the literature given rise to multiple applications However tracking coloured objects through time has an important drawback since the way in which the camera perceives the colour of the object can change Simple updates are often used to address this problem which imply a risk of distorting the model and losing the target In this paper a joint image characteristic-space tracking is proposed which updates the model simultaneously to the object location In order to avoid the curse of dimensionality a Rao-Blackwellised particle filter has been used Using this technique the hypotheses are evaluated depending on the difference between the model and the current target appearance during the updating stage Convincing results have been obtained in sequences under both sudden and gradual illumination condition changes Crown Copyright (C) 2010 Published by Elsevier B V All rights reserved

Gaussian Approximation for Tracking Occluding and Interacting Targets

In this paper, we show how interacting and occluding targets can be tackled successfully within a Gaussian approximation. For that purpose, we develop a general expansion of the mean and covariance of the posterior and we consider a first order approximation of it. The proposed method differs from EKF in that neither a non-linear dynamical model nor a non-linear measurement vector to state relation have to be defined, so it works with any kind of interaction potential and likelihood. The approach has been tested on three sequences (10400, 2500, and 400 frames each one). The results show that our approach helps to reduce the number of failures without increasing too much the computation time with respect to methods that do not take into account target interactions.

Tracking the Links to Social Functioning: Asperger Syndrome and Gaze Patterns

The application of Eye Tracking (ET) to the study of social functioning in Asperger Syndrome (AS) provides a unique perspective into social attention and cognition in this atypical neurodevelopmental group. Research in this area has shown how ET can capture social attention atypicalities within this group, such as diminished fixations to the eye region when viewing still images and movie clips; increased fixation to the mouth region; reduced face gaze. Issues exist, however, within the literature, where the type (static/dynamic) and the content (ecological validity) of stimuli used appear to affect the nature of the gaze patterns reported. Objectives: Our research aims were: using the same group of adolescents with AS, to compare their viewing patterns to age and IQ matched typically developing (TD) adolescents using stimuli considered to represent a hierarchy of ecological validity, building from static facial images; through a non-verbal movie clip; through verbal footage from real-life conversation; to eye tracking during real-life conversation. Methods: Eleven participants with AS were compared to 11 TD adolescents, matched for age and IQ. In Study 1, participants were shown 2 sets of static facial images (emotion faces, still images taken from the dynamic clips). In Study 2, three dynamic clips were presented (1 non-verbal movie clip, 2 verbal footage from real-life conversation). Study 3 was an exploratory study of eye tracking during a real-life conversation. Eye movements were recorded via a HiSpeeed (240Hz) SMI eye tracker fitted with chin and forehead rests. Various methods of analysis were used, including a paradigm for temporal analysis of the eye movement data. Results: Results from these studies confirmed that the atypical nature of social attention in AS was successfully captured by this paradigm. While results differed across stimulus sets,
collectively they demonstrated how individuals with AS failed to focus on the most socially relevant aspects of the various stimuli presented. There was also evidence that the eye movements of the AS group were atypically affected by the presence of motion and verbal information. Discriminant Function Analysis demonstrated that the ecological validity of stimuli was an important factor in identifying atypicalities associated with AS, with more accurate classifications of AS and TD groups occurring for more naturalistic stimuli (dynamic rather than static). Graphical analysis of temporal sequences of eye movements revealed the atypical manner in which AS participants followed interactions within the dynamic stimuli. Taken together with data on the order of gaze patterns, more subtle atypicalities were detected in the gaze behaviour of AS individuals towards more socially pertinent regions of the dynamic stimuli. Conclusions: These results have potentially important implications for our understanding of deficits in Asperger Syndrome, as they show that, with more naturalistic stimuli, subtle differences in social attention can be detected that

Analysis of Dependence Tracking Algorithms for Task Dataflow Execution

Processor architectures has taken a turn towards many-core processors, which integrate multiple processing cores on a single chip to increase overall performance, and there are no signs that this trend will stop in the near future. Many-core processors are harder to program than multi-core and single-core processors due to the need of writing parallel or concurrent programs with high degrees of parallelism. Moreover, many-cores have to operate in a mode of strong scaling because of memory bandwidth constraints. In strong scaling increasingly finer-grain parallelism must be extracted in order to keep all processing cores busy.

Task dataflow programming models have a high potential to simplify parallel program- ming because they alleviate the programmer from identifying precisely all inter-task de- pendences when writing programs. Instead, the task dataflow runtime system detects and enforces inter-task dependences during execution based on the description of memory each task accesses. The runtime constructs a task dataflow graph that captures all tasks and their dependences. Tasks are scheduled to execute in parallel taking into account dependences specified in the task graph.

Several papers report important overheads for task dataflow systems, which severely limits the scalability and usability of such systems. In this paper we study efficient schemes to manage task graphs and analyze their scalability. We assume a programming model that supports input, output and in/out annotations on task arguments, as well as commutative in/out and reductions. We analyze the structure of task graphs and identify versions and generations as key concepts for efficient management of task graphs. Then, we present three schemes to manage task graphs building on graph representations, hypergraphs and lists. We also consider a fourth edge-less scheme that synchronizes tasks using integers. Analysis using micro-benchmarks shows that the graph representation is not always scalable and that the edge-less scheme introduces least overhead in nearly all situations.