A person's body silhouette recorded by one or more cameras must be recognised across a wide range of potential targets to complete the person's re-identification job. The person re-identification issue is the process of matching together pictures of the same pedestrian taken with several cameras. The issue is further by the pictures' poor resolution, variations in lighting, and the appearance of held items like a bag from various vantage points. The major problems arise from the fact that several images of the same individual are taken at various points in time and with various cameras. The aligned re-id method may help with some of the problems associated with person re-identification. By using Aligned Re- ID, a global feature may be extracted and learned together with local ones. We integrate several losses to limit the model since the intra-class distance in person re-identification should be smaller than the inter-class distance. Using three common benchmark datasets, Market-1501, CUHK03 (detected and labelled), and CUHK-SYSU, we test our method's performance and find that it outperforms existing methods.
When considering the problem of disabled persons, a new concept known as Speech driven 3D face animation employing deep learning and neural networks has come into use. Nonverbal behaviour cues, such as facial expressions, are still intact and provide information about what we are thinking, doing or reacting to. When it comes to computerbased movies and digital games, expressive face animation is a must-have feature. It is necessary to get audio input from the user, after which the matching characteristics of the audio are extracted. Once the expressions have been analysed, they are combined with the intermediate 3D model to complete the process. The relevant result is generated with the assistance of the neural renderer. An overview of the whole implementation is presented in this paper.
COVID-19, also known as the Corona Virus, caused drastic changes in civilization, eventually leading to a pandemic. Many businesses were affected by the rapidly spreading Corona virus. The focus of this research is on finding a solution to avoid the transit rate. The current research focuses on the many fundamental causes of illness propagation and the technological systems' contributions to disease control. Wearing a facemask and maintaining social distance are two frequent ways to avoid the rapid spread of the disease. To determine whether or not social distancing and face mask protection are being employed, image and video processing are used. In this proposed system, we will see how we may monitor social distancing and implement face mask detection in public areas and workplaces using Python, Computer Vision and Deep Learning.
Nowadays, Liver Cancer is spreading silently at an alarming pace since liver disease has a poor survival rate and symptoms do not manifest until cancer has progressed to an advanced stage. If the illness is detected late, the typical individual has only a one-year survival rate. As a result, we propose to diagnose liver cancer via feature extraction and classification. We go through the three detection phases of processing, pre-processing, and detection, then classify the tumours based on the retrieved characteristics for normal and abnormal stages. Three steps comprise the diagnostic method: pre-processing of MRI images, feature extraction, and classification. Following image pre-processing, the picture is segmented using fuzzy C means clustering and a level-set segmenter. Additionally, features are retrieved using the Gray-Level Co-Occurrence Matrix for Texture Analysis (GLCM). Finally, a KNN classifier is used to categorise normal and pathological livers.
Blind people experience many communication difficulties, and they must overcome different barriers. Visual impairment is one of humanity's most important limitations, particularly in this day and age where information is increasingly conveyed through text messages, rather than speech, which is one of the most significant restrictions. We hope that the gadget we have suggested would be of assistance to individuals who are visually impaired. In this voicecontrolled personal assistant, the Raspberry Pi is used in conjunction with other hardware and software specs to perform tasks such as object detection, text-to-speech, and image-to-speech.
