Dataset Information

Application of Crowd Simulations in the Evaluation of Tracking Algorithms.

ABSTRACT: Tracking and action-recognition algorithms are currently widely used in video surveillance, monitoring urban activities and in many other areas. Their development highly relies on benchmarking scenarios, which enable reliable evaluations/improvements of their efficiencies. Presently, benchmarking methods for tracking and action-recognition algorithms rely on manual annotation of video databases, prone to human errors, limited in size and time-consuming. Here, using gained experiences, an alternative benchmarking solution is presented, which employs methods and tools obtained from the computer-game domain to create simulated video data with automatic annotations. Presented approach highly outperforms existing solutions in the size of the data and variety of annotations possible to create. With proposed system, a potential user can generate a sequence of random images involving different times of day, weather conditions, and scenes for use in tracking evaluation. In the design of the proposed tool, the concept of crowd simulation is used and developed. The system is validated by comparisons to existing methods.

SUBMITTER: Staniszewski M

PROVIDER: S-EPMC7506927 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Application of Crowd Simulations in the Evaluation of Tracking Algorithms.

Staniszewski Michał M Foszner Paweł P Kostorz Karol K Michalczuk Agnieszka A Wereszczyński Kamil K Cogiel Michał M Golba Dominik D Wojciechowski Konrad K Polański Andrzej A

Sensors (Basel, Switzerland) 20200902 17

Tracking and action-recognition algorithms are currently widely used in video surveillance, monitoring urban activities and in many other areas. Their development highly relies on benchmarking scenarios, which enable reliable evaluations/improvements of their efficiencies. Presently, benchmarking methods for tracking and action-recognition algorithms rely on manual annotation of video databases, prone to human errors, limited in size and time-consuming. Here, using gained experiences, an alterna ...[more]

PMID: 32887286

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Project description:Magnetic resonance imaging (MRI) allows non-invasive evaluation of inflammatory bowel disease (IBD) by assessing pathologically altered gut. Besides morphological changes, relaxation times and diffusion capacity of involved bowel segments can be obtained by MRI. The aim of this study was to assess the use of multiparametric MRI in the diagnosis of experimentally induced colitis in mice, and evaluate the diagnostic benefit of parameter combinations using machine learning. This study relied on colitis induction by Dextran Sodium Sulfate (DSS) and investigated the colon of mice in vivo as well as ex vivo. Receiver Operating Characteristics were used to calculate sensitivity, specificity, positive- and negative-predictive values (PPV and NPV) of these single values in detecting DSS-treatment as a reference condition. A Model Averaged Neural Network (avNNet) was trained on the multiparametric combination of the measured values, and its predictive capacity was compared to those of the single parameters using exact binomial tests. Within the in vivo subgroup (n = 19), the avNNet featured a sensitivity of 91.3% (95% CI: 86.6-96.0%), specificity of 92.3% (95% CI: 85.1-99.6%), PPV of 96.9% (94.0-99.9%) and NPV of 80.0% (95% CI: 69.9-90.1%), significantly outperforming all single parameters in at least 2 accuracy measures (p < 0.003) and performing significantly worse compared to none of the single values. Within the ex vivo subgroup (n = 30), the avNNet featured a sensitivity of 87.4% (95% CI: 82.6-92.2%), specificity of 82.9% (95% CI: 76.1-89.7%), PPV of 88.9% (84.3-93.5%) and NPV of 80.8% (95% CI: 73.8-87.9%), significantly outperforming all single parameters in at least 2 accuracy measures (p < 0.015), exceeded by none of the single parameters. In experimental mouse colitis, multiparametric MRI and the combination of several single measured values to an avNNet can significantly increase diagnostic accuracy compared to the single parameters alone. This pilot study will provide new avenues for the development of an MR-derived colitis score for optimized diagnosis and surveillance of inflammatory bowel disease.

Dataset Information

Application of Crowd Simulations in the Evaluation of Tracking Algorithms.

Publications

Application of Crowd Simulations in the Evaluation of Tracking Algorithms.

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