OBJECTIVES: The main objective of the research is to develop a robust and high-performance human action recognition techniques. A combination of local and holistic feature extraction methods used through analyzing the most effective features to extract to reach the objective, followed by using simple and high-performance machine learning algorithms.
METHODS: This paper presents three robust action recognition techniques based on a series of image analysis methods to detect activities in different scenes. The general scheme architecture consists of shot boundary detection, shot frame rate re-sampling, and compact feature vector extraction. This process is achieved by emphasizing variations and extracting strong patterns in feature vectors before classification.
RESULTS: The proposed schemes are tested on datasets with cluttered backgrounds, low- or high-resolution videos, different viewpoints, and different camera motion conditions, namely, the Hollywood-2, KTH, UCF11 (YouTube actions), and Weizmann datasets. The proposed schemes resulted in highly accurate video analysis results compared to those of other works based on four widely used datasets. The First, Second, and Third Schemes provides recognition accuracies of 57.8%, 73.6%, and 52.0% on Hollywood2, 94.5%, 97.0%, and 59.3% on KTH, 94.5%, 95.6%, and 94.2% on UCF11, and 98.9%, 97.8% and 100% on Weizmann.
CONCLUSION: Each of the proposed schemes provides high recognition accuracy compared to other state-of-art methods. Especially, the Second Scheme as it gives excellent comparable results to other benchmarked approaches.
METHODS: A total of 613 patients were recruited for the study from the dental clinic at the Faculty of Dentistry, Najran University, Saudi Arabia. The data collection was done in three parts from the patients who visited the hospital to receive dental treatment. The first part included the socio-demographic characteristics of the patients and the COVID-19 swab tests performed within the past 14 days. The second part was the clinical examination, and the third part was a confirmation of the swab test taken by the patient by checking the Hesen website using the patient ID. After data collection, statistical analysis was carried out using SPSS 26.0. Descriptive analysis was done and expressed as mean, standard deviation, frequency, and percentage (%). A cross-tabulation, also described as a contingency table, was used to identify trends and patterns across data and explain the correlation between different variables.
RESULTS: It was seen from the status of the swab test within 14 days of the patient's arrival at the hospital for the dental treatment that 18 (2.9%) patients lied about the pre-treatment swab test within 14 days, and 595 (97.1%) were truthful. The observed and expected counts showed across genders and diagnosis a statistically significant difference (p