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Nowadays in India, due to the increase in the number of accidents vehicles are being automated using a variety of Computer vision algorithms. This paper focuses on the detection of tail signals of cars under different illumination circumstances. This system is implemented using FAST and SIFT algorithm which helps to extract features from the images. The obtained features were optimized by using K-Means Clustering Algorithm. This huge feature vector is converted into 8 clusters. These optimized feature vectors were trained on five different classifiers as Decision Tree, SVM(RBF), Random Forest, and Voting Classifier. The trained data set used in this algorithm contains around 9052 images. The obtained accuracy results of different classifiers are as follows, Decision tress has 76.36%, SVM(RBF) has 77.91%, Random Forest has 87.52%, KNN has 89.72%, the Voting classifier has 86.52%. It is observed that KNN gives the highest accuracy among the five used classifiers. This has, to the best of the authors' knowledge, not been presented in literature before. [ABSTRACT FROM AUTHOR]

The deaths caused by road accidents are one of India's most pressing issues. The inattentiveness of the driver is responsible for almost 80% of all collisions. Mobile phone use, conversing with passengers, reaching behind the wheel to grab something, and drinking while driving are just a few of the factors that can cause a driver to become distracted. This project aims to create a reliable and effective system for detecting and classifying distracted drivers. The efficacy of CNNs in the field of computer vision was the inspiration for this paper, which proposes a paradigm based on Convolutional Neural Network which not only senses but also recognizes the driver who is distracted. For this purpose it is using a publicly available dataset from the Kaggle website as an input for this model. There are several different forms of distractions, but this study focuses on the manual distraction, which is focused on the driver's stance. The goal of this work is to keep an eye on the driver's actions and seek for diversion signs. Distractions are divided into two groups in the model: safe driving and unsafe or distracted driving. For this purpose, various architectures of CNN can be used. This work, however, employs sequential architecture and achieved the highest observed accuracy of 99% with a total of 9.5M trainable parameters. The primary goal of this research is to classify a test image into two states of the driver that have been considered. [ABSTRACT FROM AUTHOR]

Devanagari is an ancient script that is used to write Hindi, Nepali, Marathi, Maithili, Awadhi, Newari, and Bhojpuri, among other Indo-Aryan languages. Thousands of individuals in India use this script to write documents in Marathi and Hindi. Indian mythology is based on this script. Because of the script's prominence, handwritten Devanagari character identification has grown in popularity over time. Handwritten recognition of languages such as English has received a lot of attention, but Indian languages written in the Devanagari script are also a rich source of information. Most of the work on this problem statement has been done either using deep neural networks like CNN at its heart coupled with other machine learning techniques like SVM,Random Forest etc. In this paper we are utilising a recently introduced transformer model for computer vision known as Vision Transformer for the task of Devanagari Character Recognition. We have also compared our model with various pretrained CNN-based architectures like ResNet50,VGG16 and InceptionV3 and ViT has outperformed these models both on DHCD dataset and the modified slightly more complex version of it with accuracy scores of 99.68% on the original testing dataset of the DHCD dataset and accuracy score of 96.55% on the modified(blurred) slightly more complex version of the original testing dataset. The ViT model thus generalized better than standard CNN-based models on the problem of Devanagari Character recognition. [ABSTRACT FROM AUTHOR]

In recent years, the human population has reached its peak in both the developing countries and developed countries like India and China. An increase in personnel density ended up being a hard task to maintain or manage people flow in socio-public places. However, people flow management is out of control for humans, which may also cause some safety damages. This paper aims to give an optimal solution for people flow with the help of machine learning, deep learning, and also AI-cameras are used for counting the number of people entering and leaving a user-defined place. Here, also the centroid tracking and centroid detecting algorithms are used to count the number of people together with their direction. This technique helps to replace manual security,in-store traffic management, and much more. [ABSTRACT FROM AUTHOR]

We are now living in the era of E-commerce where people can access their favourite products with a mere click. But E-commerce has its pros and cons, and the flip side is when it comes to the tactile feel and the physical inspection of a product. Although, the E-commerce market has had a huge impact, it cannot be denied that people still love to shop in retail outlets like supermarkets and shopping malls. The major drawback with retail shopping is the long time taken for the billing. If truth be told the time spent at the billing counter seems to be more than the time spent for the actual choice of products in the shop. The panacea can be in the use of AI or Artificial Intelligence. Soon artificial intelligence is definitely going to make a drastic change in the field of marketing and having an impact on customer behavior as well. In this paper we propose a framework that consists of a Computer vision driven AI approach which will reduce the time of billing to nearly fourfold. It is an automatic billing and payment system which will greatly reduce the time spent for payment of the bill. A specific UPI QR code will be generated for each bill which can be used by the customer and paid through Google Pay. [ABSTRACT FROM AUTHOR]

Cotton is one of the important cash crops and plays a good role in the agricultural economy of India and it provides livelihood to more than 6 million farmers. But one major disadvantage is that cotton crop is highly prone to pests and diseases and approximately 30-35% of the yield gets corrupted because of this. Therefore, early detection of diseases is necessary because detecting diseases at an advance stage leads to crop failure. Thus, utilising the advancement in computer vision and machine learning can be of great benefit to the agriculture sector. This paper focuses on application of Mask-RCNN object detection algorithm which is based on instance segmentation to identify the pests and diseases on cotton leaves. [ABSTRACT FROM AUTHOR]