Cyclist route assessment using machine learning

Increasing the number of bike commutes can provide numerous benefits for individuals and communities. However, several factors including the availability of cycle paths, traffic characteristics, and pavement quality, can either encourage or discourage the use of bicycles. To promote cycling and understand how cyclists interact with the urban environment, it is crucial to assess the quality of cyclist routes. This thesis proposes an automatic assessment tool that uses machine learning to detect features of the route segment and calculates a score representing the level of safety and comfort for cyclists. The models are trained on YOLOv5 to classify pavement types, detect pavement defects and detect the presence of cycle paths. Two datasets were built and annotated for the pavement type classification and cycle infrastructure detection tasks. A questionnaire was applied to cyclists to compare the real perceptions with the automatic assessment. The results showed a good alignment with the real perceptions, validating the approach, but also demonstrated the need of adding new features and improving the models’ performance before being adequate for real use.Aumentar o n´umero de deslocamentos de bicicleta pode trazer in´umeros benef´ıcios para indiv´ıduos e comunidades. No entanto, v´arios fatores, incluindo a disponibilidade de ciclovias, caracter´ısticas do tr´afego e qualidade do pavimento, podem encorajar ou desencorajar o uso de bicicletas. Para promover o ciclismo e entender como os ciclistas interagem com o ambiente urbano, ´e crucial avaliar a qualidade das rotas dos ciclistas. Esta tese prop˜oe uma ferramenta de avalia¸c˜ao autom´atica que usa aprendizado de m´aquina para detectar caracter´ısticas do segmento de rota e calcula uma pontua¸c˜ao que representa o n´ıvel de seguran¸ca e conforto para os ciclistas. Os modelos s˜ao treinados no YOLOv5 para classificar os tipos de pavimento, detectar defeitos no pavimento e detectar a presen¸ca de ciclovias. Dois datasets foram constru´ıdos e anotados para as tarefas de classifica¸c˜ao do tipo de pavimento e detec¸c˜ao de infraestrutura cicl´avel. Foi aplicado um question´ario aos ciclistas para comparar as percep¸c˜oes reais com a avalia¸c˜ao autom´atica. Os resultados mostraram um bom alinhamento com as percep¸c˜oes reais, validando a abordagem, mas tamb´em demonstraram a necessidade de adicionar novas caracter´ısticas e melhorar a performance dos modelos antes de ser adequado para uso real

Detect-and-Track: Efficient Pose Estimation in Videos

This paper addresses the problem of estimating and tracking human body keypoints in complex, multi-person video. We propose an extremely lightweight yet highly effective approach that builds upon the latest advancements in human detection and video understanding. Our method operates in two-stages: keypoint estimation in frames or short clips, followed by lightweight tracking to generate keypoint predictions linked over the entire video. For frame-level pose estimation we experiment with Mask R-CNN, as well as our own proposed 3D extension of this model, which leverages temporal information over small clips to generate more robust frame predictions. We conduct extensive ablative experiments on the newly released multi-person video pose estimation benchmark, PoseTrack, to validate various design choices of our model. Our approach achieves an accuracy of 55.2% on the validation and 51.8% on the test set using the Multi-Object Tracking Accuracy (MOTA) metric, and achieves state of the art performance on the ICCV 2017 PoseTrack keypoint tracking challenge.Comment: In CVPR 2018. Ranked first in ICCV 2017 PoseTrack challenge (keypoint tracking in videos). Code: https://github.com/facebookresearch/DetectAndTrack and webpage: https://rohitgirdhar.github.io/DetectAndTrack

3D Convolution Neural Networks for Medical Imaging; Classification and Segmentation : A Doctor’s Third Eye

Master's thesis in Information- and communication technology (IKT591)In this thesis, we studied and developed 3D classification and segmentation models for medical imaging. The classification is done for Alzheimer’s Disease and segmentation is for brain tumor sub-regions. For the medical imaging classification task we worked towards developing a novel deep architecture which can accomplish the complex task of classifying Alzheimer’s Disease volumetrically from the MRI scans without the need of any transfer learning. The experiments were performed for both binary classification of Alzheimer’s Disease (AD) from Normal Cognitive (NC), as well as multi class classification between the three stages of Alzheimer’s called NC, AD and Mild cognitive impairment (MCI). We tested our model on the ADNI dataset and achieved mean accuracy of 94.17% and 89.14% for binary classification and multiclass classification respectively. In the second part of this thesis which is segmentation of tumors sub-regions in brain MRI images we studied some popular architecture for segmentation of medical imaging and inspired from them, proposed our architecture of end-to-end trainable fully convolutional neural net-work which uses attention block to learn the localization of different features of the multiple sub-regions of tumor. Also experiments were done to see the effect of weighted cross-entropy loss function and dice loss function on the performance of the model and the quality of the output segmented labels. The results of evaluation of our model are received through BraTS’19 dataset challenge. The model is able to achieve a dice score of 0.80 for the segmentation of whole tumor, and a dice scores of 0.639 and 0.536 for other two sub-regions within the tumor on validation data. In this thesis we successfully applied computer vision techniques for medical imaging analysis. We show the huge potential and numerous benefits of deep learning to combat and detect diseases opens up more avenues for research and application for automating medical imaging analysis