The focus of the planned study is on finding lost people in crowded environments including public events, festivals, temples, and meetings. In today's busy environments, single-person identification is a challenging endeavor. This problem is addressed by applying a deep learning idea to arrive at a workable solution. Individuals are recognized through the use of a Convolutional Neural Network (CNN). Several face characteristics are used to positively identify the missing person. The use of Face Detection is crucial to the success of this endeavor. The ImageNet Large-Scale Visual Recognition Challenge participant AlexNet is used. The main takeaway is that the model's depth is crucial to its excellent performance, which is computationally expensive but is made possible by the use of graphics processing units (GPUs) during training. With sufficient training using a wide variety of images, it is possible to locate the sought-after object in the allotted space. The project's momentum is based on watching the live feed. Faces are extracted from the video and saved to a database. A collection of photos is available for use in making identifications. We use our own dataset to train the AlexNet's many layers. The images in the database are labelled according to whether or not they contain a person by utilizing the pretrained network. Additionally, the KLT method should be used to determine the person's location and enable real-time tracking.

Missing Person Detection Using AI

Each year, a large number of youngsters are found missing in India. Among them, a large number of cases are never solved due to various difficulties faced by the police ranging from heavy paperwork to lacking technology. Therefore, one of this work’s key goals is to provide an application that may assist people whose children have been missing and rescued by the public. This will also reduce the time required to find the missing child to reunite the child with their loved ones as soon as possible. The pictures of child victims can be uploaded by the citizens along with landmarks, to our web app. The photographs will be matched to the missing child’s registered photographs if existing in the database. A deep neural network model is trained to locate the lost youngster using a facial picture uploaded by the citizens. Multi-Tasking CNN (MTCNN), the most efficient DNN technique for image-based apps, is used for facial Identification. The images were passed through an augmentation layer to get images of different orientations, brightness, and contrast, which were used ahead to train the EfficientNetB0 model. This model is then used to recognize faces in photographs. Using the MTCNN model for facial recognition with EfficientNetB0 and developing it yields a deep learning model that is free from all types of distortion. The model’s training accuracy is 96.66 percent and its testing accuracy is 76.81 percent, implying that there is approximately 77 percent possibility of finding a match for the missing kid. It was evaluated using 25 Child classes. Each Child class has around 15 to 20 images. These images are taken with different backgrounds and real-time settings so that model will work even when noise is present in the image.

Lost + Found: The Lost Angel Investigator

This study looks at the possibility of increasing identification abilities in closed-circuit video (CCTV) systems using a deep learning-based missing person detection model. Missing individuals are becoming increasingly common, necessitating the development of novel search tactics. This study uses deep learning to augment traditional CCTV systems by using an improved model that can reliably identify and track persons who go missing. Taking a detailed look at current techniques to missing person identification, the literature review shows the shortcomings of existing systems and the potential for improving them through the use of deep learning. The paper examines past methodologies, including facial recognition accuracy, tracking robustness, and system scalability.The integration of computer vision with missing person identification, object tracking technologies, and facial recognition algorithms are among the main topics. The research also investigates privacy, moral, and legal implications of deploying such technology in public.

A Survey of Integrating Deep Learning-Based Missing Person Detection Model into CCTV Systems for Enhanced Identification

The Aadhaar-Enabled Biometric Web Portal for Finding Missing Persons research aims to address the critical issue of locating missing individuals by leveraging Aadhaar-enabled biometric data. With the widespread adoption of Aadhaar cards in India, this research uses the unique biometric information linked to Aadhaar cards to create a web portal. Through this portal, users can input relevant biometric data, such as fingerprints or iris scans, which are then compared with the Aadhaar database. The system utilizes advanced biometric matching algorithms to accurately identify potential matches, thereby aiding law enforcement agencies and concerned individuals in locating missing persons efficiently. By integrating Aadhaar-enabled biometrics into a user-friendly web interface, this research facilitates swift and accurate identification, offering a valuable tool in the effort to reunite missing individuals with their families and loved ones.

A Web Portal for Tracking and Finding Missing Person Using Aadhaar Enabled Biometrics

In the world, a countless number of people are missing every day which includes kids, teens, mentally challenged, old-aged people with Alzheimer’s, etc. Most of them remain untraced.[1] This paper proposes a system that would help the police and the public by accelerating the process of searching using face recognition. When a person goes missing, the people related to that person or the police can upload the picture of the person which will get stored in the database. The face recognition model in our system will try to find a matching the database with the help of face encodings. It is performed by comparing the face encodings of the frames in the video streaming to the face encodings of the images in the database. If a match is found, it will be notified to the police and the people related to that person with the details of the person found. This project is really helpful as concerned for the government in order to lessen the time and man power spent in finding the missing persons. The face recognition model that we have developed maintains an accuracy of 94%.

Nowadays, finding a missing person is a difficult task despite the fact that we are all kept up to date by social media. It requires a great deal of paper work and is a time-consuming process additionally, even after all of this hard labour there are minimal aspects of obtaining a satisfactory result. This project provides a mechanism that benefits both the police and the normal public by speeding up the procedure. Here we used face recognition as a search process. If a person goes missing, the individual's guardian can upload the photograph which will then be stored in the system. The facial recognition model in our system will then search the database for a match of that individual. If there is a match is found, the police and the guardian will be alerted.

Implementation of Machine Learning and KNN Algorithm for Finding Missing Person

In the recent year we have been experiencing a huge surge on internet, due to many people have started using internet. Nowadays there are many paid and fake reviews flooding the e-commerce websites like Amazon, Flipkart and many other e-commerce websites. In which many customers make decisions based on these fake reviews or comments provided by others who had similar experiences. In today's competitive environment, anyone may write anything, which has resulted in an increase in the number of bogus reviews. So, to overcome this problem of fake reviews we will propose a machine learning model. The model will be trained with 40000 datasets to predict the fake reviews. Machine learning is the one of the trending techniques to predicts the output based on the experience. Furthermore, we can test the model using testing data which is also a part of the data set. We are using Passive Aggressive Classifier and we have achieved an accuracy of 83.53%.

Implementation Of Fake Review Detection Using Passive Aggressive Classifier

Nowadays agriculture and processes are getting more intelligent mechanisms to improve the yield and reduce manual work. Smart agriculture provides numerous modern ideologies to farmers. But still, farmers face one important issue crop disease. Many researchers provide plenty of ways to recover and tackle the situation to come out of this problem. Therefore, they proceed with image processing to identify diseases from rice plant images. Farmers mainly face problems to take proper images for classification. Because of various reasons like various environmental factors, farmers ignorance, field size, capturing angle, device limitations, etc. are affecting the quality of the disease detection system, and these factors degrade overall performance. For this problem, introducing the Intelligent multi-dimensional tiling (IMDT) technique with an advanced convolution neural network with cognitive object detection (CNN-COD). IMDT technique developed with an intelligent expert system that adjusts input image size, capturing angles and other factors automatically. This advanced tiling technique supports to do the cropping and fluttering of input images for resizing. And CNN-COD model was used to calculate rice leaf width size and rescaled at the time of image segmentation with the Residual network (ResNet) model. Created dynamic tiled images are uniformly and scaled dimensional objects. These input values are used to train the CNN-COD rice plant disease, prediction model. Our proposed models were appraised on more than 4960 images which contain 8 various types of rice crop diseases. The experimental result portrayed out the CNN-COD model receives significant improvement in objection detection and image classification for the rice plant disease detection system. Mean average precision (MAP) values compared the CNN-COD model with the YOLOv4 model it got improved by 3.7% with the tiled input dataset.

An Automatic Rice Plant Disease Detection Model Built With Unstructured Data Using IMDT Tiling and CNN Cognitive Object Detection

The issue of visual debilitation or visual deficiency is confronted around the world. As per insights of the World Health Organization (WHO), worldwide, somewhere around 2.2 billion individuals have a dream weakness or visual deficiency, of whom no less than 1 billion are visually impaired. As far as territorial contrasts, the pervasiveness of vision disability in low-and center pay districts is multiple times higher than in big league salary locales. Blind individuals by and large need to depend on white sticks, guide canines, screen-understanding programming, magnifiers, and glasses to help them for versatility, be that as it may, to help the visually impaired individuals the visual world must be changed into the sound world with the possibility to illuminate them about objects as well as their spatial areas. In this manner, we propose to help the outwardly debilitated by presenting a framework that is generally possible, smaller, and financially savvy. The goal of the project is to develop a model an object recognition model to detect and recognize the object for visually impaired people and it can also be used in other markets. Existing techniques for object detection requires a large training data it takes more time and the process is complicated. In this project we propose computer vision concept to recognize object and convert it into text by using the pre trained dataset and the texts are converted further to speech. The system will also support multiple object detection and building a one line description of the object which can be either describe the position of the object or the color. The proposed system can further be implemented in any portable devices for the visually impaired people to recognize objects.

Implementation Of Object Detection and Identification For Visually Impaired

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Papers

Implementation of Machine Learning and KNN Algorithm for Finding Missing Person

Implementation Of Fake Review Detection Using Passive Aggressive Classifier

An Automatic Rice Plant Disease Detection Model Built With Unstructured Data Using IMDT Tiling and CNN Cognitive Object Detection

Implementation Of Object Detection and Identification For Visually Impaired