The growth of the number of people in the monitoring scene may increase the probability of security threat, which makes crowd counting more and more important. Most of the existing approaches estimate the number of pedestrians within one frame, which results in inconsistent predictions in terms of time. This paper, for the first time, introduces a quadratic programming model with the network flow constraints to improve the accuracy of crowd counting. Firstly, the foreground of each frame is segmented into groups, each of which contains several pedestrians. Then, a regression-based map is developed in accordance with the relationship between low-level features of each group and the number of people in it. Secondly, a directed graph is constructed to simulate constraints on people's flow, whose vertices represent groups of each frame and arcs represent people moving from one group to another. Then, the people flow can be viewed as an integer flow in the constructed digraph. Finally, by solving a quadratic programming problem with network flow constraints in the directed graph, we obtain consistency in people counting. The experimental results show that the proposed method can reduce the crowd counting errors and improve the accuracy. Moreover, this method can also be applied to any ultramodern group-based regression counting approach to get improvements.
Background
We aim to create a holistic competency-based assessment system to measure competency evolution over time – one of the first such systems in China.
Method
Two rounds of self-reported surveys were fielded among the graduates from the Shantou University Medical College: June through December 2017, and May through August 2018. Responses from three cohorts of graduates specializing in clinical medicine – new graduates, resident physicians, and senior physicians – were analyzed. Gaps between respondents’ expected and existing levels of competencies were examined using a modified service quality model, SERVQUAL
Results
A total of 605 questionnaires were collected in 2017 for the construction of competency indicators and a 5-level proficiency rating scale, and 407 in 2018, for confirmatory factor and competency gap analysis. Reliability coefficients of all competency indicators (36) were greater than 0.9. Three competency domains were identified through exploratory factor analysis: knowledge (K), skills (S), and attitude (A). The confirmatory factor analysis confirmed the fit of the scale (CMIN/DF < 4; CFI > 0.9; IFI > 0.9; RMSEA ≤ 0.08). Within the cohorts of resident and senior physicians, the largest competency gap was seen in the domain of knowledge (K): −1.84 and −1.41, respectively. Among new graduates, the largest gap was found in the domain of skills (S) (−1.92), with the gap in knowledge (−1.91) trailing closely behind.
Conclusions
A competency-based assessment system is proposed to evaluate clinician’s competency development in three domains: knowledge (K), skills (S), and attitude (A). The system consists of 36 competency indicators, a rating scale of 5 proficiency levels, and a gap analysis to measure competency evolution through 3 key milestones in clinician’s professional career: new graduate, resident physician, and senior physician. The competency gaps identified can provide evidence-based guide to clinicians’ own continuous development as well as future medical curriculum improvements.
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