Learning Background-Aware Correlation Filters for Visual Tracking

Correlation Filters (CFs) have recently demonstrated excellent performance in terms of rapidly tracking objects under challenging photometric and geometric variations. The strength of the approach comes from its ability to efficiently learn - "on the fly" - how the object is changing over time. A fundamental drawback to CFs, however, is that the background of the object is not be modelled over time which can result in suboptimal results. In this paper we propose a Background-Aware CF that can model how both the foreground and background of the object varies over time. Our approach, like conventional CFs, is extremely computationally efficient - and extensive experiments over multiple tracking benchmarks demonstrate the superior accuracy and real-time performance of our method compared to the state-of-the-art trackers including those based on a deep learning paradigm

Analysis and Selection of a Remote Docking Simulation Visual Display System

The development of a remote docking simulation visual display system is examined. Video system and operator performance are discussed as well as operator command and control requirements and a design analysis of the reconfigurable work station

Fast, Dense Feature SDM on an iPhone

In this paper, we present our method for enabling dense SDM to run at over 90 FPS on a mobile device. Our contributions are two-fold. Drawing inspiration from the FFT, we propose a Sparse Compositional Regression (SCR) framework, which enables a significant speed up over classical dense regressors. Second, we propose a binary approximation to SIFT features. Binary Approximated SIFT (BASIFT) features, which are a computationally efficient approximation to SIFT, a commonly used feature with SDM. We demonstrate the performance of our algorithm on an iPhone 7, and show that we achieve similar accuracy to SDM

The Growth of Population in Victorian Colwall

This short article examines the increase in population in a Herefordshire parish between 1851 and 1901. One major factor in this process appears to have been the influx of wealthy people (with large numbers of servants) seeking an attractive environment with easy access to Birmingham and London

Beverly Lewis House – Setting up a safe house for women with learning disabilities who have experienced abuse

In dem Beitrag wird über ein Frauenhaus in London (Großbritannien) berichtet, in dem ausschließlich Frauen mit Lernschwierigkeiten Zuflucht vor Mißbrauch finden und in dem sie ggf. frühere Mißbrauchserfahrungen aufarbeiten können. Beverly Lewis House, das von der Autorin geleitet wird, ist in seiner Form einzigartig. Der Beitrag gibt einen Einblick in die Bedeutung und in die Strukturen eines solchen Hauses. Dazu werden sechs Bereiche beschrieben, die wichtig sind, um ein Frauenhaus speziell für Frauen mit Lernschwierigkeiten in die Wirklichkeit umzusetzen: die Architektur und die Strukturen des Hauses, die Finanzierung, das Team, die Angebote des Hauses, wie ein therapeutisches Umfeld geschaffen wird und wie es für die Frauen weitergeht. Außerdem wird darauf eingegangen, wie die Mitarbeiterinnen mit Verhaltensweisen der Frauen umgehen, die in Folge der Mißbrauchserfahrungen bei ihnen aufgetreten sind, wie zum Beispiel Eßstörungen, selbstverletzende Verhaltensweisen oder Depressionen. Der Beitrag ist weitgehend in „einfacher Sprache“ geschrieben, damit die Informationen auch für Menschen mit Lernschwierigkeiten zugänglich sind.(DIPF/Orig.)This contribution introduces a battered women shelter in London, Great Britain which exclusively provides refuge to women with learning disabilities who experienced sexualized violence. In the case of sexual abuse in early childhood psychotherapy is provided. Beverly Lewis House is the only institution of its kind and is lead by the author of this article. The contribution gives an insight into the significance and the structures of such an institution. Six components will be described that are important for implementing a battered women shelter specified on serving women with a learning disability: the architecture and the structures of the institution, the funding, the team, the variety of offers, the establishment of a therapeutic climate and the further progress of the women after leaving the Beverly Lewis House. Furthermore, the staff are being instructed how to deal with behaviours resulting from abuse experiences such as eating disorders, self-harming behaviour and depressions. The contribution is predominantly written in “simple language” in order to make it accessible to people with learning disabilities.(DIPF/Orig.

FIL Benchmarking Project

Write up of a workshop which took place at Interlend 2015, the annual conference of the Forum for Interlending and Information Delivery, held in Manchester. The workshop followed on from the presentation of the results of the FIL Benchmarking Project Survey in 2014, and was intended to facilitate discussion of the findings, with a view to establishing best practice

Incorporating Feedback from Multiple Sensory Modalities Enhances Brain–Machine Interface Control

The brain typically uses a rich supply of feedback from multiple sensory modalities to control movement in healthy individuals. In many individuals, these afferent pathways, as well as their efferent counterparts, are compromised by disease or injury resulting in significant impairments and reduced quality of life. Brain–machine interfaces (BMIs) offer the promise of recovered functionality to these individuals by allowing them to control a device using their thoughts. Most current BMI implementations use visual feedback for closed-loop control; however, it has been suggested that the inclusion of additional feedback modalities may lead to improvements in control. We demonstrate for the first time that kinesthetic feedback can be used together with vision to significantly improve control of a cursor driven by neural activity of the primary motor cortex (MI). Using an exoskeletal robot, the monkey\u27s arm was moved to passively follow a cortically controlled visual cursor, thereby providing the monkey with kinesthetic information about the motion of the cursor. When visual and proprioceptive feedback were congruent, both the time to successfully reach a target decreased and the cursor paths became straighter, compared with incongruent feedback conditions. This enhanced performance was accompanied by a significant increase in the amount of movement-related information contained in the spiking activity of neurons in MI. These findings suggest that BMI control can be significantly improved in paralyzed patients with residual kinesthetic sense and provide the groundwork for augmenting cortically controlled BMIs with multiple forms of natural or surrogate sensory feedback

Improving Brain–Machine Interface Performance by Decoding Intended Future Movements

Objective. A brain–machine interface (BMI) records neural signals in real time from a subject\u27s brain, interprets them as motor commands, and reroutes them to a device such as a robotic arm, so as to restore lost motor function. Our objective here is to improve BMI performance by minimizing the deleterious effects of delay in the BMI control loop. We mitigate the effects of delay by decoding the subject\u27s intended movements a short time lead in the future. Approach. We use the decoded, intended future movements of the subject as the control signal that drives the movement of our BMI. This should allow the user\u27s intended trajectory to be implemented more quickly by the BMI, reducing the amount of delay in the system. In our experiment, a monkey (Macaca mulatta) uses a future prediction BMI to control a simulated arm to hit targets on a screen. Main Results. Results from experiments with BMIs possessing different system delays (100, 200 and 300 ms) show that the monkey can make significantly straighter, faster and smoother movements when the decoder predicts the user\u27s future intent. We also characterize how BMI performance changes as a function of delay, and explore offline how the accuracy of future prediction decoders varies at different time leads. Significance. This study is the first to characterize the effects of control delays in a BMI and to show that decoding the user\u27s future intent can compensate for the negative effect of control delay on BMI performance