1,200 research outputs found
Robust Visual Tracking by Exploiting the Historical Tracker Snapshots
© 2015 IEEE. Variations of target appearances due to illumination changes, heavy occlusions and abrupt motions are the major factors for tracking failures. In this paper, we show that these failures can be effectively handled by exploiting the trajectory consistency between the current tracker and its historical trained snapshots. Here, we propose a Scale-adaptive Multi-Expert (SME) tracker, which combines the current tracker and its historical trained snapshots to construct a multi-expert ensemble. The best expert in the ensemble is then selected according to the accumulated trajectory consistency criteria. The base tracker estimates the translation accurately with regression based correlation filter, and an effective scale adaptive scheme is introduced to handle scale changes on-the-fly. SME is extensively evaluated on the 51 sequences tracking benchmark and VOT2015 dataset. The experimental results demonstrate the excellent performance of the proposed approach against state-of-the-art methods with real-time speed
Frequency Tracking and Parameter Estimation for Robust Quantum State-Estimation
In this paper we consider the problem of tracking the state of a quantum
system via a continuous measurement. If the system Hamiltonian is known
precisely, this merely requires integrating the appropriate stochastic master
equation. However, even a small error in the assumed Hamiltonian can render
this approach useless. The natural answer to this problem is to include the
parameters of the Hamiltonian as part of the estimation problem, and the full
Bayesian solution to this task provides a state-estimate that is robust against
uncertainties. However, this approach requires considerable computational
overhead. Here we consider a single qubit in which the Hamiltonian contains a
single unknown parameter. We show that classical frequency estimation
techniques greatly reduce the computational overhead associated with Bayesian
estimation and provide accurate estimates for the qubit frequencyComment: 6 figures, 13 page
Extracting Secrets from Encrypted Virtual Machines
AMD SEV is a hardware extension for main memory encryption on multi-tenant
systems. SEV uses an on-chip coprocessor, the AMD Secure Processor, to
transparently encrypt virtual machine memory with individual, ephemeral keys
never leaving the coprocessor. The goal is to protect the confidentiality of
the tenants' memory from a malicious or compromised hypervisor and from memory
attacks, for instance via cold boot or DMA. The SEVered attack has shown that
it is nevertheless possible for a hypervisor to extract memory in plaintext
from SEV-encrypted virtual machines without access to their encryption keys.
However, the encryption impedes traditional virtual machine introspection
techniques from locating secrets in memory prior to extraction. This can
require the extraction of large amounts of memory to retrieve specific secrets
and thus result in a time-consuming, obvious attack. We present an approach
that allows a malicious hypervisor quick identification and theft of secrets,
such as TLS, SSH or FDE keys, from encrypted virtual machines on current SEV
hardware. We first observe activities of a virtual machine from within the
hypervisor in order to infer the memory regions most likely to contain the
secrets. Then, we systematically extract those memory regions and analyze their
contents on-the-fly. This allows for the efficient retrieval of targeted
secrets, strongly increasing the chances of a fast, robust and stealthy theft.Comment: Accepted for publication at CODASPY 201
Data-Driven Application Maintenance: Views from the Trenches
In this paper we present our experience during design, development, and pilot
deployments of a data-driven machine learning based application maintenance
solution. We implemented a proof of concept to address a spectrum of
interrelated problems encountered in application maintenance projects including
duplicate incident ticket identification, assignee recommendation, theme
mining, and mapping of incidents to business processes. In the context of IT
services, these problems are frequently encountered, yet there is a gap in
bringing automation and optimization. Despite long-standing research around
mining and analysis of software repositories, such research outputs are not
adopted well in practice due to the constraints these solutions impose on the
users. We discuss need for designing pragmatic solutions with low barriers to
adoption and addressing right level of complexity of problems with respect to
underlying business constraints and nature of data.Comment: Earlier version of paper appearing in proceedings of the 4th
International Workshop on Software Engineering Research and Industrial
Practice (SER&IP), IEEE Press, pp. 48-54, 201
End-to-end Flow Correlation Tracking with Spatial-temporal Attention
Discriminative correlation filters (DCF) with deep convolutional features
have achieved favorable performance in recent tracking benchmarks. However,
most of existing DCF trackers only consider appearance features of current
frame, and hardly benefit from motion and inter-frame information. The lack of
temporal information degrades the tracking performance during challenges such
as partial occlusion and deformation. In this work, we focus on making use of
the rich flow information in consecutive frames to improve the feature
representation and the tracking accuracy. Firstly, individual components,
including optical flow estimation, feature extraction, aggregation and
correlation filter tracking are formulated as special layers in network. To the
best of our knowledge, this is the first work to jointly train flow and
tracking task in a deep learning framework. Then the historical feature maps at
predefined intervals are warped and aggregated with current ones by the guiding
of flow. For adaptive aggregation, we propose a novel spatial-temporal
attention mechanism. Extensive experiments are performed on four challenging
tracking datasets: OTB2013, OTB2015, VOT2015 and VOT2016, and the proposed
method achieves superior results on these benchmarks.Comment: Accepted in CVPR 201
Monocular tracking of the human arm in 3D: real-time implementation and experiments
We have developed a system capable of tracking a human arm in 3D and in real time. The system is based on a previously developed algorithm for 3D tracking which requires only a monocular view and no special markers on the body. In this paper we describe our real-time system and the insights gained from real-time experimentation
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