Inferring 3D trajectory from monocular data using deep learning

Sammanfattning: Trajectory estimation, with regards to reconstructing a 3D trajectory from a 2D trajectory, is commonly achieved using stereo or multi camera setups. Although projections from 3D to 2D suffer significant information loss, some methods approach this problem from a monocular perspective to address limitations of multi camera systems, such as requiring points in to be observed by more than one camera. This report explores how deep learning methodology can be applied to estimation of golf balls’ 3D trajectories using features from synthetically generated monocular data. Three neural network architectures for times series analysis, Long Short-Term Memory (LSTM), Bidirectional LSTM(BLSTM), and Temporal Convolutional Network (TCN); are compared to a simpler Multi Layer Perceptron (MLP) baseline and theoretical stereo error. The results show the models’ performances are varied with median performances often significantly better than average, caused by some predictions with very large errors. Overall the BLSTM performed best of all models both quantitatively and qualitatively, for some ranges with a lower error than a stereo estimate with an estimated disparity error of 1. Although the performance of the proposed monocular approaches do not outperform a stereo system with a lower disparity error, the proposed approaches could be good alternatives where stereo solutions might not be possible.