Faba bean (Vicia faba L.), a drought-sensitive crop, is drastically affected by drought stresses compromising its growth and yield. However, wild relatives of faba bean are considered a reservoir of potential genetic resources for tolerance against abiotic stresses. This study was conducted to characterize wild relatives of faba bean for identification of a specific tolerance system required for its improvement against drought stress. The study focused on physiological, biochemical, and anatomical responses of wild Vicia species under drought stress conditions. The experiment was carried out under various levels of drought stress imposed through different field capacities (FC) which included 80% FC ie (well-watered condition), 55% FC (moderate stress), and 30% FC (severe stress). When compared to plants grown in a control environment, drought stress significantly reduced the studied physiological attributes including soluble sugars (21.3% and 15.8%), protein contents (14.7 and 14.6%), and chlorophyll (8.4 and 28.6%) under moderate (55% FC) and severe drought stress (30% FC), respectively. However, proline content increased by 20.5% and 27.6%, peroxidase activity by 48.5% and 57.1%, and superoxide dismutase activity by 72.6% and 64.8% under moderate and severe stress, respectively. The studied anatomical attributes were also affected under drought stress treatments, including diameter of stem xylem vessels (9.1% and 13.7%), leaf lower epidermal thickness (8.05% and 13.34%), and leaf phloem width (5.3% and 10.1%) under moderate and severe stress, respectively. Wild Vicia spp. showed better tolerance to water-deficit conditions as compared to cultivated Vicia L. The observed potential diversity for drought tolerance in wild Vicia spp. may assist in improvement of faba bean and may also help in understanding the mechanisms of adaptations in drought-prone environments.

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Video monitoring has been widely used in various places, for example, tourist attractions, stations, terminals, offices, supermarkets, minimarkets, and other places. The use of video monitoring has the aim to improve security aspects in a place that is considered quite helpful. The rapid development of technology, video monitoring has been implemented for purposes other than for security, such as people counting systems. People counting to find out the number of visitors in a place or building is a difficult job to do, requires a lot of time and often the data obtained is not appropriate. Gaussian Mixture Model (GMM) is a background reduction method, which is used to identify the background and foreground. Background reduction is an approach that is widely used to detect moving objects on video from static cameras. While blob detection is detecting a group of pixels connected in an image that has a different colour (white or black). Blob is used for object classification, whether the object is a person or not. From the results of system testing shows that counting people can be done well, with an average percentage of 95.83% recall, 94.5% precision and 90.88% accuracy of the entire video test data. The use of GMM in counting people in this study can also count people who carry objects properly as long as they are not drawn as in the morning test video. In addition, this system can store the calculated data.