Improving Global Multi-target Tracking with Local Updates

Conference dates: September 6-7 & 12, 2014We propose a scheme to explicitly detect and resolve ambiguous situations in multiple target tracking. During periods of uncertainty, our method applies multiple local single target trackers to hypothesise short term tracks. These tracks are combined with the tracks obtained by a global multi-target tracker, if they result in a reduction in the global cost function. Since tracking failures typically arise when targets become occluded, we propose a local data association scheme to maintain the target identities in these situations. We demonstrate a reduction of up to 50% in the global cost function, which in turn leads to superior performance on several challenging benchmark sequences. Additionally, we show tracking results in sports videos where poor video quality and frequent and severe occlusions between multiple players pose difficulties for state-of-the-art trackers.Anton Milan, Rikke Gade, Anthony Dick, Thomas B. Moeslund, and Ian Rei

Comparison of 3 Different Perioperative Care Models for Patients With Hip Fractures Within 1 Health Service

INTRODUCTION: Orthogeriatric care models have been introduced within many health-care facilities to improve outcomes for hip fracture patients. This study aims to evaluate differences in care between 3 models, an orthopedic model, a geriatric model, and a comanaged model. MATERIALS AND METHODS: A retrospective analysis was conducted for hip fracture patients treated at Western Health between November 2012 and March 2014. All patients aged 65 years or older were included in the analysis. RESULTS: There were 183 patients in the orthopedic model, 137 in the geriatric model, and 126 in the comanaged model. Demographics and clinical characteristics were similar across the 3 models. Length of stay, mortality, and discharge destination were also consistent across the 3 groups. However, groups involving geriatricians were more likely to receive preoperative medical assessments, have greater recognition of postoperative medical problems, and have implementation of long-term osteoporosis management. CONCLUSION: The involvement of geriatricians in perioperative care models resulted in more comprehensive medical care without impacting length of stay, mortality, or discharge destination

Convex optimisation-based methods for K-complex detection

K-complex is a special type of electroencephalogram (EEG, brain activity) waveform that is used in sleep stage scoring. An automated detection of K-complexes is a desirable component of sleep stage monitoring. This automation is difficult due to the ambiguity of the scoring rules, complexity and extreme size of data. We develop three convex optimisation models that extract key features of EEG signals. These features are essential for detecting K-complexes. Our models are based on approximation of the original signals by sine functions with piecewise polynomial amplitudes. Then, the parameters of the corresponding approximations (rather than raw data) are used to detect the presence of K-complexes. The proposed approach significantly reduces the dimension of the classification problem (by extracting essential features) and the computational time while the classification accuracy is improved in most cases. Numerical results show that these models are efficient for detecting K-complexes

Privacy preserving multi-target tracking

Automated people tracking is important for a wide range of applications. However, typical surveillance cameras are controversial in their use, mainly due to the harsh intrusion of the tracked individuals' privacy. In this paper, we explore a privacy-preserving alternative for multi-target tracking. A network of infrared sensors attached to the ceiling acts as a low-resolution, monochromatic camera in an indoor environment. Using only this low-level information about the presence of a target, we are able to reconstruct entire trajectories of several people. Inspired by the recent success of offline approaches to multi-target tracking, we apply an energy minimization technique to the novel setting of infrared motion sensors. To cope with the very weak data term from the infrared sensor network we track in a continuous state space with soft, implicit data association. Our experimental evaluation on both synthetic and real-world data shows that our principled method clearly outperforms previous techniques.Anton Milan, B, Stefan Roth, Konrad Schindler, and Mineichi Kud

Green Synthesis: The Antibacterial and Photocatalytic Potential of Silver Nanoparticles Using Extract of <i>Teucrium stocksianum</i>

Green synthesis is one of the promising pathways for biologically active nanoscale materials. Herein, an eco-friendly synthesis of silver nanoparticles (SNPs) was carried out using an extract of Teucrium stocksianum. The biological reduction and size of NPS were optimized by controlling the physicochemical parameters such as concentration, temperature, and pH. A comparison of fresh and air-dried plant extracts was also undertaken to establish a reproducible methodology. The biosynthesized SNPs were characterized by UV–Vis spectroscopy, FT-IR, SEM, DLS, and XRD analyses. The prepared SNPs exhibited significant biological potential against multi-drug-resistant pathogenic strains. The results revealed that the biosynthesized SNPs exhibit high antimicrobial activity at low concentrations compared to the parent plant extract. Minimum inhibition concentration (MIC) values were found between 5.3 µg/mL to 9.7 µg/mL for the biosynthesized SNPs, whereas the aqueous extract of the plant showed many high values of MIC, i.e., between 69 and 98 µg/ML. Furthermore, the synthesized SNPs were found efficient in the photolytic degradation of methylene blue under sunlight