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Automatic leaf disease detection techniques are effective for reducing the time-consuming effort of monitoring large crop farms and early identification of disease symptoms of plant leaves. Although crop tomatoes are seen to be susceptible to a variety of diseases that can reduce the production of the crop. In recent years, advanced deep learning methods show successful applications for plant disease detection based on observed symptoms on leaves. However, these methods have some limitations. This study proposed a high-performance tomato leaf disease detection approach, namely attention-based dilated CNN logistic regression (ADCLR). Firstly, we develop a new feature extraction method using attention-based dilated CNN to extract most relevant features in a faster time. In our preprocessing, we use Bilateral filtering to handle larger features to make the image smoother and the Ostu image segmentation process to remove noise in a fast and simple way. In this proposed method, we preprocess the image with bilateral filtering and Otsu segmentation. Then, we use the Conditional Generative Adversarial Network (CGAN) model to generate a synthetic image from the image which is preprocessed in the previous stage. The synthetic image is generated to handle imbalance and noisy or wrongly labeled data to obtain good prediction results. Then, the extracted features are normalized to lower the dimensionality. Finally, extracted features from preprocessed data are combined and then classified using fast and simple logistic regression (LR) classifier. The experimental outcomes show the state-of-the-art performance on the Plant Village database of tomato leaf disease by achieving 100%, 100%, 96.6% training, testing, and validation accuracy, respectively, for multiclass. From the experimental analysis, it is clearly demonstrated that the proposed multimodal approach can be utilized to detect tomato leaf disease precisely, simply and quickly. We have a potential plan to improve the model to make it cloud-based automated leaf disease classification for different plants.

Researchers have recently focused their attention on vision-based hand gesture recognition. However, due to several constraints, achieving an effective vision-driven hand gesture recognition system in real time has remained a challenge. This paper aims to uncover the limitations faced in image acquisition through the use of cameras, image segmentation and tracking, feature extraction, and gesture classification stages of vision-driven hand gesture recognition in various camera orientations. This paper looked at research on vision-based hand gesture recognition systems from 2012 to 2022. Its goal is to find areas that are getting better and those that need more work. We used specific keywords to find 108 articles in well-known online databases. In this article, we put together a collection of the most notable research works related to gesture recognition. We suggest different categories for gesture recognition-related research with subcategories to create a valuable resource in this domain. We summarize and analyze the methodologies in tabular form. After comparing similar types of methodologies in the gesture recognition field, we have drawn conclusions based on our findings. Our research also looked at how well the vision-based system recognized hand gestures in terms of recognition accuracy. There is a wide variation in identification accuracy, from 68% to 97%, with the average being 86.6 percent. The limitations considered comprise multiple text and interpretations of gestures and complex non-rigid hand characteristics. In comparison to current research, this paper is unique in that it discusses all types of gesture recognition techniques.

Almost two million Muslim pilgrims from all around the globe visit Mecca each year to conduct Hajj. Each year, the number of pilgrims grows, creating worries about how to handle such large crowds and avoid unpleasant accidents or crowd congestion catastrophes. In this paper, we introduced deep Hajj crowd dilated convolutional neural network (DHCDCNNet) for crowd density analysis. This research also presents augmentation technique to create additional dataset based on the hajj pilgrimage scenario. We utilized a single framework to extract both high-level and low-level features. For creating additional dataset we divide the process of images augmentation into two routes. In the first route, we utilized magnitude extraction followed by the polar magnitude. In the second route, we performed morphological operation followed by transforming the image into skeleton. This paper presented a solution to the challenge of measuring crowd density using a surveillance camera pointed at a distance. An FCNN-based technique for crowd analysis is included in the proposed methodology, particularly for classifying crowd density. There are several obstacles in video analysis when there are a large number of pilgrims moving around the tawaf area, with densities of between 7 and 8 per square meter. The proposed DHCDCNNet method has achieved accuracy of 97%, 89% and 100% for the JHU-CROWD dataset, the UCSD dataset and the proposed Hajj-Crowd dataset, respectively. The proposed Hajj-Crowd dataset, the UCSD dataset, and the JHU-CROW dataset all had accuracy of 98%, 97% and 97%, respectively, using the VGGNet approach. Using the ResNet50 approach, the proposed Hajj-Crowd dataset, the UCSD dataset, and the JHU-CROW dataset all had an accuracy of 99%, 91% and 97%, respectively.

This article discusses an effective technique for detecting abnormalities in Hajj crowd videos. In order to guarantee the identification of anomalies in scenes, a trained and supervised FCNN is turned into an FCNN using FCNNs and temporal data. By minimizing computational complexity, incorrect movement detection is utilized to achieve high performance in terms of speed and precision. This FCNN-based architecture is designed to handle two primary tasks: feature representation and the detection of incorrect movement outliers. Additionally, to overcome the aforementioned issues, this research will generate a new crowd anomaly video dataset based on the Hajj pilgrimage scenario. On the proposed dataset, the UCSD Ped2, Subway Entry, and Subway Exit datasets, the proposed FCNN-based technique obtained ultimate accuracy of 100%, 90%, 95%, and 89%, respectively. Additionally, the ResNet50-based technique achieved ultimate accuracy of 96%, 89%, 94%, and 92%, respectively, for the proposed dataset, the UCSD Ped2, Subway Entry, and Subway Exit datasets.

Background: This paper focuses on advances in crowd control study with an emphasis on high-density crowds, particularly Hajj crowds. Video analysis and visual surveillance have been of increasing importance in order to enhance the safety and security of pilgrimages in Makkah, Saudi Arabia. Hajj is considered to be a particularly distinctive event, with hundreds of thousands of people gathering in a small space, which does not allow a precise analysis of video footage using advanced video and computer vision algorithms. This research proposes an algorithm based on a Convolutional Neural Networks model specifically for Hajj applications. Additionally, the work introduces a system for counting and then estimating the crowd density. Methods: The model adopts an architecture which detects each person in the crowd, spots head location with a bounding box and does the counting in our own novel dataset (HAJJ-Crowd). Results: Our algorithm outperforms the state-of-the-art method, and attains a remarkable Mean Absolute Error result of 200 (average of 82.0 improvement) and Mean Square Error of 240 (average of 135.54 improvement). Conclusions: In our new HAJJ-Crowd dataset for evaluation and testing, we have a density map and prediction results of some standard methods.

Satellite imagery offers potential for applications in disaster response, business recommendation, agricultural decision-making, and urban planning, owing to its accessibility and ability to capture details of the Earth’s surface over time. However, traditional methods struggle to extract information from these images. This study leverages deep learning and fusion of visual features from satellite imagery to determine suitable businesses for specific locations in Malaysia. By scrutinizing the surrounding structure and characteristics, this study examines the visual context of business environments. Satellite and map images are processed by transfer learning deep learning models to extract deep features, while hand-crafted statistical features and Scale-Invariant Feature Transform (SIFT) features are extracted from road network images to represent structural patterns of business locations. It is found that combined feature sets are more reliable than individual feature sets for business recommendation from satellite imagery. This study analyzed 12,500 satellite images across five classes, generating four feature sets of 128 and 512 dimensions. The 512-dimensional concatenated feature set achieved the highest accuracy: 0.5932 with an artificial neural network (0.13 improvement) and 0.6068 with a support vector machine (0.25 improvement) over the best individual feature sets.

This research enhances crowd analysis by focusing on excessive crowd analysis and crowd density predictions for Hajj and Umrah pilgrimages. Crowd analysis usually analyzes the number of objects within an image or a frame in the videos and is regularly solved by estimating the density generated from the object location annotations. However, it suffers from low accuracy when the crowd is far away from the surveillance camera. This research proposes an approach to overcome the problem of estimating crowd density taken by a surveillance camera at a distance. The proposed approach employs a fully convolutional neural network (FCNN)-based method to monitor crowd analysis, especially for the classification of crowd density. This study aims to address the current technological challenges faced in video analysis in a scenario where the movement of large numbers of pilgrims with densities ranging between 7 and 8 per square meter. To address this challenge, this study aims to develop a new dataset based on the Hajj pilgrimage scenario. To validate the proposed method, the proposed model is compared with existing models using existing datasets. The proposed FCNN based method achieved a final accuracy of 100%, 98%, and 98.16% on the proposed dataset, the UCSD dataset, and the JHU-CROWD dataset, respectively. Additionally, The ResNet based method obtained final accuracy of 97%, 89%, and 97% for the proposed dataset, UCSD dataset, and JHU-CROWD dataset, respectively. The proposed Hajj-Crowd-2021 crowd analysis dataset and the model outperformed the other state-of-the-art datasets and models in most cases.

Gathering a large number of people in a shared physical area is very common in urban culture. Although there are limitless examples of mega crowds, the Islamic religious ritual, the Hajj, is considered as one of the greatest crowd scenarios in the world. The Hajj is carried out once in a year with a congregation of millions of people when the Muslims visit the holy city of Makkah at a given time and date. Such a big crowd is always prone to public safety issues, and therefore requires proper measures to ensure safe and comfortable arrangement. Through the advances in computer vision based scene understanding, automatic analysis of crowd scenes is gaining popularity. However, existing crowd analysis algorithms might not be able to correctly interpret the video content in the context of the Hajj. This is because the Hajj is a unique congregation of millions of people crowded in a small area, which can overwhelm the use of existing video and computer vision based sophisticated algorithms. Through our studies on crowd analysis, crowd counting, density estimation, and the Hajj crowd behavior, we faced the need of a review work to get a research direction for abnormal behavior analysis of Hajj pilgrims. Therefore, this review aims to summarize the research works relevant to the broader field of video analytics using deep learning with a special focus on the visual surveillance in the Hajj. The review identifies the challenges and leading-edge techniques of visual surveillance in general, which may gracefully be adaptable to the applications of Hajj and Umrah. The paper presents detailed reviews on existing techniques and approaches employed for crowd analysis from crowd videos, specifically the techniques that use deep learning in detecting abnormal behavior. These observations give us the impetus to undertake a painstaking yet exhilarating journey on crowd analysis, classification and detection of any abnormal movement of the Hajj pilgrims. Furthermore, because the Hajj pilgrimage is the most crowded domain for video-related extensive research activities, this study motivates us to critically analyze the crowd on a large scale.

In computer vision, identifying anomalies in crowded Hajj situations is challenging due to severe inter-object occlusions, varied crowd concentrations, and complex dynamics of the human mass. Two primary goals are addressed by this FCNN-based architecture: feature representation and wrong movement outlier identification. We proposed a crowd anomaly hajj monitor (CAHM) method using video sequences of crowded scenes to identify and localize abnormal behavior. The main contribution of our approach is the combination of anomaly detection-based optical flow features and classification based on spatial-temporal features using fully convolutional neural networks (FCNN), which has never been achieved in the past based on our extensive research in this domain. Using FCNN and spatial-temporal data, a pre-trained supervised FCNN detects (global) anomalies in Hajj crowded scenes. Additionally, this research intends to develop a new dataset based on the Hajj pilgrimage scenario in order to solve the issues. This architecture enables us, through spatial-temporal convolutions, to capture features in both spatial and time dimensions and to extract knowledge about the presence and motion of features encrypted in continuous frames. Two primary goals are addressed by this FCNN-based architecture: feature representation and cascade outlier identification. The suggested technique outperforms current methods in terms of detection and localization accuracy, as shown in the experimental results on the benchmark datasets. We employ the UCSD and Subway dataset in our study and present the Hajj-Crowd-2021 as a new video dataset. Extensive testing on the proposed Hajj-Crowd-2021 anomaly dataset demonstrates that it provides cutting-edge recognition performance and outstanding durability in crowd analysis. To verify our work, we used the available datasets to compare the proposed model to current models. In all of these situations, our model outperforms. Crowd analysis, for instance, improves classification accuracy by achieving 96% in highly crowded crowd situations. Furthermore, crowd anomaly analysis improves classification accuracy for the UCSD, Ped1, and Ped2 datasets for extremely congested crowd situations by 89%, 83%, and 85%, respectively. On the other hand, our method has improved classification accuracy for the Subway dataset by 88% for the exit, and 86% for the entrance case.

Satellite images are widely used in various fields as they are important, especially in monitoring activities. Change detection is one such activity that assesses differences in satellite images over time. However, weather and environmental effects may degrade the quality of images. Therefore, the image quality needs to be enhanced before processing the image. This study examined a number of adaptive contrast enhancement approaches based on histogram & non-linear transfer functions, as well as the impact of adopting multiple colour spaces for enhancement. We proposed an enhancement method where the L channel in the CIE LAB colour space was enhanced through a combination of the adaptive gamma correction method with weighting distribution (AGCWD) and Contrast Limited Adaptive Histogram Equalisation (CLAHE). We also advocated using average ranking to select the best method by averaging the various metrics. Improving the performance of change detection, our technique produced the highest average rank of BRISQUE and RMSE contrast values compared to the other methods.