Simple Online and Realtime Tracking with a Deep Association Metric

Simple Online and Realtime Tracking (SORT) is a pragmatic approach to multiple object tracking with a focus on simple, effective algorithms. In this paper, we integrate appearance information to improve the performance of SORT. Due to this extension we are able to track objects through longer periods of occlusions, effectively reducing the number of identity switches. In spirit of the original framework we place much of the computational complexity into an offline pre-training stage where we learn a deep association metric on a large-scale person re-identification dataset. During online application, we establish measurement-to-track associations using nearest neighbor queries in visual appearance space. Experimental evaluation shows that our extensions reduce the number of identity switches by 45%, achieving overall competitive performance at high frame rates.Comment: 5 pages, 1 figur

Initial Development toward Non-Invasive Drug Monitoring via Untargeted Mass Spectrometric Analysis of Human Skin.

Drug monitoring is crucial for providing accurate and effective care; however, current methods (e.g., blood draws) are inconvenient and unpleasant. We aim to develop a non-invasive method for the detection and monitoring of drugs via human skin. The initial development toward this aim required information about which drugs, taken orally, can be detected via the skin. Untargeted liquid chromatography-mass spectrometry (LC-MS) was used as it was unclear if drugs, known drug metabolites, or other transformation products were detectable. In accomplishing our aim, we analyzed samples obtained by swabbing the skin of 15 kidney transplant recipients in five locations (forehead, nasolabial area, axillary, backhand, and palm), bilaterally, on two different clinical visits. Untargeted LC-MS data were processed using molecular networking via the Global Natural Products Social Molecular Networking platform. Herein, we report the qualitative detection and location of drugs and drug metabolites. For example, escitalopram/citalopram and diphenhydramine, taken orally, were detected in forehead, nasolabial, and hand samples, whereas N-acetyl-sulfamethoxazole, a drug metabolite, was detected in axillary samples. In addition, chemicals associated with environmental exposure were also detected from the skin, which provides insight into the multifaceted chemical influences on our health. The proof-of-concept results presented support the finding that the LC-MS and data analysis methodology is currently capable of the qualitative assessment of the presence of drugs directly via human skin

Multiple Object Tracking in Urban Traffic Scenes with a Multiclass Object Detector

Multiple object tracking (MOT) in urban traffic aims to produce the trajectories of the different road users that move across the field of view with different directions and speeds and that can have varying appearances and sizes. Occlusions and interactions among the different objects are expected and common due to the nature of urban road traffic. In this work, a tracking framework employing classification label information from a deep learning detection approach is used for associating the different objects, in addition to object position and appearances. We want to investigate the performance of a modern multiclass object detector for the MOT task in traffic scenes. Results show that the object labels improve tracking performance, but that the output of object detectors are not always reliable.Comment: 13th International Symposium on Visual Computing (ISVC

Multiple Space Object Tracking Using A Randomized Hypothesis Generation Technique

In order to protect assets and operations in space, it is critical to collect and maintain accurate information regarding Resident Space Objects (RSOs). This collection of information is typically known as Space Situational Awareness (SSA). Ground-based and space-based sensors provide information regarding the RSOs in the form of observations or measurement returns. However, the distance between RSO and sensor can, at times, be tens of thousands of kilometers. This and other factors lead to noisy measurements that, in turn, cause one to be uncertain about which RSO a measurement belongs to. These ambiguities are known as data association ambiguities. Coupled with uncertainty in RSO state and the vast number of objects in space, data association ambiguities can cause the multiple space object-tracking problem to become computationally intractable. Tracking the RSO can be framed as a recursive Bayesian multiple object tracking problem with state space containing both continuous and discrete random variables. Using a Finite Set Statistics (FISST) approach one can derive the Random Finite Set (RFS) based Bayesian multiple object tracking recursions. These equations, known as the FISST multiple object tracking equations, are computationally intractable when solved in full. This computational intractability provokes the idea of the newly developed alternative hypothesis dependent derivation of the FISST equations. This alternative derivation allows for a Markov Chain Monte Carlo (MCMC) based randomized sampling technique, termed Randomized FISST (R-FISST). R-FISST is found to provide an accurate approximation of the full FISST recursions while keeping the problem tractable. There are many other benefits to this new derivation. For example, it can be used to connect and compare the classical tracking methods to the modern FISST based approaches. This connection clearly defines the relationships between different approaches and shows that they result in the same formulation for scenarios with a fixed number of objects and are very similar in cases with a varying number of objects. Findings also show that the R-FISST technique is compatible with many powerful optimization tools and can be scaled to solve problems such as collisional cascading

Search for the rare decays B0→J/ψγB^{0}\to J/\psi \gamma and Bs0→J/ψγB^{0}_{s} \to J/\psi \gamma

A search for the rare decay of a $B^{0}$ or $B^{0}_{s}$ meson into the final state $J/\psi\gamma$ is performed, using data collected by the LHCb experiment in $pp$ collisions at $\sqrt{s}=7$ and $8$ TeV, corresponding to an integrated luminosity of 3 fb$^{-1}$. The observed number of signal candidates is consistent with a background-only hypothesis. Branching fraction values larger than $1.7\times 10^{-6}$ for the $B^{0}\to J/\psi\gamma$ decay mode are excluded at 90% confidence level. For the $B^{0}_{s}\to J/\psi\gamma$ decay mode, branching fraction values larger than $7.4\times 10^{-6}$ are excluded at 90% confidence level, this is the first branching fraction limit for this decay.Comment: All figures and tables, along with any supplementary material and additional information, are available at https://lhcbproject.web.cern.ch/lhcbproject/Publications/LHCbProjectPublic/LHCb-PAPER-2015-044.htm

Quark helicity distributions in the nucleon for up, down, and strange quarks from semi--inclusive deep--inelastic scattering

Polarized deep--inelastic scattering data on longitudinally polarized hydrogen and deuterium targets have been used to determine double spin asymmetries of cross sections. Inclusive and semi--inclusive asymmetries for the production of positive and negative pions from hydrogen were obtained in a re--analysis of previously published data. Inclusive and semi--inclusive asymmetries for the production of negative and positive pions and kaons were measured on a polarized deuterium target. The separate helicity densities for the up and down quarks and the anti--up, anti--down, and strange sea quarks were computed from these asymmetries in a ``leading order'' QCD analysis. The polarization of the up--quark is positive and that of the down--quark is negative. All extracted sea quark polarizations are consistent with zero, and the light quark sea helicity densities are flavor symmetric within the experimental uncertainties. First and second moments of the extracted quark helicity densities in the measured range are consistent with fits of inclusive data