No abstract

In this paper, three approaches for ego-motion estimation using Time-of-Flight (ToF) camera data are evaluated. Ego-motion is defined as a process of estimating a camera's pose relative to some initial pose using the camera's image sequence. The ToF camera is characterised with a number error models. These models are used to design several filters that are applied on point cloud data. Iterative Closest Point (ICP) is applied on the consecutive range images of the ToF camera to estimate relative pose transform which is used for egomotion estimation. We implemented two variants of ICP namely point-to-point and point-to-plane. A feature based ego-motion approach that detects and tracks features on the amplitude images and use their corresponding 3D points to estimate the relative transformation is implemented. These approaches are evaluated using the groundtruth provided by the vicon system.

Deep learning has gained traction due its supremacy in terms of accuracy and ability to automatically learn features from input data. However, deep learning algorithms can sometimes be flawed due to many factors such as training dataset, parameters, and choice of algorithms. Few studies have evaluated the robustness of deep learning distracted driver detection algorithms. The studies evaluate the algorithms on a single dataset and do not consider cross-dataset performance. A problem arises because cross-dataset performance often implies model generalisation ability. Deploying a model in the real world without knowing its cross-dataset performance could lead to catastrophic events. The paper investigates the cross-dataset performance of deep learning distracted driver detection algorithms. Experimental results found reveal that deep learning distracted driver detection algorithms do not generalise well on unknown datasets for CNN models that use the whole image for prediction. The cross-dataset performance evaluations shed light on future research in developing robust deep learning distracted driver detection algorithms.