9,985 research outputs found
Robust Correlation Tracking for UAV with Feature Integration and Response Map Enhancement
Recently, correlation filter (CF)-based tracking algorithms have attained extensive interest in the field of unmanned aerial vehicle (UAV) tracking. Nonetheless, existing trackers still struggle with selecting suitable features and alleviating the model drift issue for online UAV tracking. In this paper, a robust CF-based tracker with feature integration and response map enhancement is proposed. Concretely, we develop a novel feature integration method that comprehensively describes the target by leveraging auxiliary gradient information extracted from the binary representation. Subsequently, the integrated features are utilized to learn a background-aware correlation filter (BACF) for generating a response map that implies the target location. To mitigate the risk of model drift, we introduce saliency awareness in the BACF framework and further propose an adaptive response fusion strategy to enhance the discriminating capability of the response map. Moreover, a dynamic model update mechanism is designed to prevent filter contamination and maintain tracking stability. Experiments on three public benchmarks verify that the proposed tracker outperforms several state-of-the-art algorithms and achieves a real-time tracking speed, which can be applied in UAV tracking scenarios efficiently
A stochastic flow rule for granular materials
There have been many attempts to derive continuum models for dense granular
flow, but a general theory is still lacking. Here, we start with Mohr-Coulomb
plasticity for quasi-2D granular materials to calculate (average) stresses and
slip planes, but we propose a "stochastic flow rule" (SFR) to replace the
principle of coaxiality in classical plasticity. The SFR takes into account two
crucial features of granular materials - discreteness and randomness - via
diffusing "spots" of local fluidization, which act as carriers of plasticity.
We postulate that spots perform random walks biased along slip-lines with a
drift direction determined by the stress imbalance upon a local switch from
static to dynamic friction. In the continuum limit (based on a Fokker-Planck
equation for the spot concentration), this simple model is able to predict a
variety of granular flow profiles in flat-bottom silos, annular Couette cells,
flowing heaps, and plate-dragging experiments -- with essentially no fitting
parameters -- although it is only expected to function where material is at
incipient failure and slip-lines are inadmissible. For special cases of
admissible slip-lines, such as plate dragging under a heavy load or flow down
an inclined plane, we postulate a transition to rate-dependent Bagnold
rheology, where flow occurs by sliding shear planes. With different yield
criteria, the SFR provides a general framework for multiscale modeling of
plasticity in amorphous materials, cycling between continuum limit-state stress
calculations, meso-scale spot random walks, and microscopic particle
relaxation
Aggregation signature for small object tracking
Small object tracking becomes an increasingly important task, which however
has been largely unexplored in computer vision. The great challenges stem from
the facts that: 1) small objects show extreme vague and variable appearances,
and 2) they tend to be lost easier as compared to normal-sized ones due to the
shaking of lens. In this paper, we propose a novel aggregation signature
suitable for small object tracking, especially aiming for the challenge of
sudden and large drift. We make three-fold contributions in this work. First,
technically, we propose a new descriptor, named aggregation signature, based on
saliency, able to represent highly distinctive features for small objects.
Second, theoretically, we prove that the proposed signature matches the
foreground object more accurately with a high probability. Third,
experimentally, the aggregation signature achieves a high performance on
multiple datasets, outperforming the state-of-the-art methods by large margins.
Moreover, we contribute with two newly collected benchmark datasets, i.e.,
small90 and small112, for visually small object tracking. The datasets will be
available in https://github.com/bczhangbczhang/.Comment: IEEE Transactions on Image Processing, 201
A facility to Search for Hidden Particles (SHiP) at the CERN SPS
A new general purpose fixed target facility is proposed at the CERN SPS
accelerator which is aimed at exploring the domain of hidden particles and make
measurements with tau neutrinos. Hidden particles are predicted by a large
number of models beyond the Standard Model. The high intensity of the SPS
400~GeV beam allows probing a wide variety of models containing light
long-lived exotic particles with masses below (10)~GeV/c,
including very weakly interacting low-energy SUSY states. The experimental
programme of the proposed facility is capable of being extended in the future,
e.g. to include direct searches for Dark Matter and Lepton Flavour Violation.Comment: Technical Proposa
Design and theoretical analysis of advanced power based positioning in RF system
Accurate locating and tracking of people and resources has become a fundamental requirement for many applications. The global navigation satellite systems (GNSS) is widely used. But its accuracy suffers from signal obstruction by buildings, multipath fading, and disruption due to jamming and spoof. Hence, it is required to supplement GPS with inertial sensors and indoor localization schemes that make use of WiFi APs or beacon nodes. In the GPS-challenging or fault scenario, radio-frequency (RF) infrastructure based localization schemes can be a fallback solution for robust navigation. For the indoor/outdoor transition scenario, we propose hypothesis test based fusion method to integrate multi-modal localization sensors. In the first paper, a ubiquitous tracking using motion and location sensor (UTMLS) is proposed. As a fallback approach, power-based schemes are cost-effective when compared with the existing ToA or AoA schemes. However, traditional power-based positioning methods suffer from low accuracy and are vulnerable to environmental fading. Also, the expected accuracy of power-based localization is not well understood but is needed to derive the hypothesis test for the fusion scheme. Hence, in paper 2-5, we focus on developing more accurate power-based localization schemes. The second paper improves the power-based range estimation accuracy by estimating the LoS component. The ranging error model in fading channel is derived. The third paper introduces the LoS-based positioning method with corresponding theoretical limits and error models. In the fourth and fifth paper, a novel antenna radiation-pattern-aware power-based positioning (ARPAP) system and power contour circle fitting (PCCF) algorithm are proposed to address antenna directivity effect on power-based localization. Overall, a complete LoS signal power based positioning system has been developed that can be included in the fusion scheme --Abstract, page iv
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