Great progress has been achieved in computer vision tasks within image and video; however, technological advances in LiDAR sensors have created a whole new area of computer vision research devoted to it. With applications in many industries, such as transportation, agriculture, or healthcare. This thesis studies object tracking in 3D point clouds. Pairs of point cloud observations are feed to a neural network to estimate pose and translation between the observations. Then these estimations, together with external features, are processed with Kalman Filter and RNN to extract spatial-temporal redundancies and improve the results. The system has been tested in the KITTI dataset, with pre-segmented observations, on different types of objects and paths. The results show that the neural network estimated pose gives a very accurate tracking. Still, the best results are achieved when combining the estimated pose and translations with a recurrent neural network.
Great progress has been achieved in computer vision tasks within image and video; however, technological advances in LiDAR sensors have created a whole new area of computer vision research devoted to it. With applications in many industries, such as transportation, agriculture, or healthcare. This thesis studies object tracking in 3D point clouds. Pairs of point cloud observations are feed to a neural network to estimate pose and translation between the observations. Then these estimations, together with external features, are processed with Kalman Filter and RNN to extract spatial-temporal redundancies and improve the results. The system has been tested in the KITTI dataset, with pre-segmented observations, on different types of objects and paths. The results show that the neural network estimated pose gives a very accurate tracking. Still, the best results are achieved when combining the estimated pose and translations with a recurrent neural network.
Great progress has been achieved in computer vision tasks within image and video; however, technological advances in LiDAR sensors have created a whole new area of computer vision research devoted to it. With applications in many industries, such as transportation, agriculture, or healthcare. This thesis studies object tracking in 3D point clouds. Pairs of point cloud observations are feed to a neural network to estimate pose and translation between the observations. Then these estimations, together with external features, are processed with Kalman Filter and RNN to extract spatial-temporal redundancies and improve the results. The system has been tested in the KITTI dataset, with pre-segmented observations, on different types of objects and paths. The results show that the neural network estimated pose gives a very accurate tracking. Still, the best results are achieved when combining the estimated pose and translations with a recurrent neural network.
