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We present the first event-based learning approach for motion segmentation in indoor scenes and the first event-based dataset – EV-IMO – which includes accurate pixel-wise motion masks, egomotion and ground truth depth. Our approach is based on an efficient implementation of the SfM learning pipeline using a low parameter neural network architecture on event data. In addition to camera egomotion and a dense depth map, the network estimates independently moving object segmentation at the pixel-level and computes per-object 3D translational velocities of moving objects. We also train a shallow network with just 40k parameters, which is able to compute depth and egomotion. Our EV-IMO dataset features 32 minutes of indoor recording with up to 3 fast moving objects in the camera field of view. The objects and the camera are tracked using a VICON motion capture system. By 3D scanning the room and the objects, ground truth of the depth map and pixel-wise object masks are obtained. We then train and evaluate our learning pipeline on EV-IMO and demonstrate that it is well suited for scene constrained robotics applications.
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Modeling Drivers’ Situational Awareness from Eye Gaze for Driving Assistance
Intelligent driving assistance can alert drivers to objects in their environment; however, such systems require a model of drivers' situational awareness (SA) (what aspects of the scene they are already aware of) to avoid unnecessary alerts. Moreover, collecting the data to train such an SA model is challenging: being an internal human cognitive state, driver SA is difficult to measure, and non-verbal signals such as eye gaze are some of the only outward manifestations of it. Traditional methods to obtain SA labels rely on probes that result in sparse, intermittent SA labels unsuitable for modeling a dense, temporally correlated process via machine learning. We propose a novel interactive labeling protocol that captures dense, continuous SA labels and use it to collect an object-level SA dataset in a VR driving simulator. Our dataset comprises 20 unique drivers' SA labels, driving data, and gaze (over 320 minutes of driving) which will be made public. Additionally, we train an SA model from this data, formulating the object-level driver SA prediction problem as a semantic segmentation problem. Our formulation allows all objects in a scene at a timestep to be processed simultaneously, leveraging global scene context and local gaze-object relationships together. Our experiments show that this formulation leads to improved performance over common sense baselines and prior art on the SA prediction task.
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- Award ID(s):
- 1943072
- PAR ID:
- 10615759
- Publisher / Repository:
- 8th Annual Conference on Robot Learning (CORL)
- Date Published:
- Format(s):
- Medium: X
- Location:
- Munich, Germany
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
