Mapping and Localization in Urban Environments Using Cameras

In this work we present a system to fully automatically create a highly accurate visual feature map from image data aquired from within a moving vehicle. Moreover, a system for high precision self localization is presented. Furthermore, we present a method to automatically learn a visual descriptor. The map relative self localization is centimeter accurate and allows autonomous driving

A Face Recognition Method Using Deep Learning To Identify Mask And Unmask Objects

At the present, the use of face masks is growing day by day and it is mandated in most places across the world. People are encouraged to cover their faces when in public areas to avoid the spread of infection which can minimize the transmission of Covid-19 by 65 percent (according to the public health officials). So, it is important to detect people not wearing face masks. Additionally, face recognition has been applied to a wide area for security verification purposes since its performance, accuracy, and reliability [15] are better than any other traditional techniques like fingerprints, passwords, PINs, and so on. In recent years, facial recognition is becoming a challenging task because of various occlusions or masks like the existence of sunglasses, scarves, hats, and the use of make-up or disguise ingredients. So, the face recognition accuracy rate is affected by these types of masks. Moreover, the use of face masks has made conventional facial recognition technology ineffective in many scenarios, such as face authentication, security check, tracking school, and unlocking phones and laptops. As a result, we proposed a solution, Masked Facial Recognition (MFR) which can identify masked and unmasked people so individuals wearing a face mask do not need to take it out to authenticate themselves. We used the Deep Learning model, Inception ResNet V1 to train our model. The CASIA dataset [17] is applied for training images and the LFW (Labeled Faces in the Wild) dataset [18] with artificial marked faces are used for model evaluation purposes. The training and testing masked datasets are created using a Computer Vision-based approach (Dlib). We received an accuracy of around 96 percent for our three different trained models. As a result, the purposed work could be utilized effortlessly for both masked and unmasked face recognition and detection systems that are designed for safety and security verification purposes without any challenges

Lypsylehmien tunnistaminen ja seuranta konenäön avulla

Improving the monitoring of health and well-being of dairy cows through the use of computer vision based systems is a topic of ongoing research. A reliable and low-cost method for identifying cow individuals would enable automatic detection of stress, sickness or injury, and the daily observation of the animals would be made easier. Neural networks have been used successfully in the identification of cow individuals, but methods are needed that do not require incessant annotation work to generate training datasets when there are changes within a group. Methods for person re-identification and tracking have been researched extensively, with the aim of generalizing beyond the training set. These methods have been found suitable also for re-identifying and tracking previously unseen dairy cows in video frames. In this thesis, a metric-learning based re-identification model pre-trained on an existing cow dataset is compared to a similar model that has been trained on new video data recorded at Luke Maaninka research farm in Spring 2021, which contains 24 individually labelled cow individuals. The models are evaluated in tracking context as appearance descriptors in Kalman filter based tracking algorithm. The test data is video footage from a separate enclosure in Maaninka and a group of 24 previously unseen cow individuals. In addition, a simple procedure is proposed for the automatic labeling of cow identities in images based on RFID data collected from cow ear tags and feeding stations, and the known feeding station locations