Deep Tracking on the Move: Learning to Track the World from a Moving Vehicle using Recurrent Neural Networks
By Julie Dequaire, Dushyant Rao, Peter Ondruska, Dominic Zeng Wang, Ingmar Posner. 2016
20 citations recorded by the source corpus.
This paper presents an end-to-end approach for tracking static and dynamic objects for an autonomous vehicle driving through crowded urban environments. Unlike traditional approaches to tracking, this method is learned end-to-end, and is able to directly predict a full unoccluded occupancy grid map from raw laser input data. Inspired by the recently presented DeepTracking approach [Ondruska, 2016], we employ a recurrent neural network (RNN) to capture the temporal evolution of the state of the environment, and propose to use Spatial Transformer modules to exploit estimates of the egomotion of the vehicle. Our results demonstrate the ability to track a range of objects, including cars, buses, pedestrians, and cyclists through occlusion, from both moving and stationary platforms, using a single learned model. Experimental results demonstrate that the model can also predict the future states of objects from current inputs, with greater accuracy than previous work.
Subfields: Automotive Engineering, Building and Construction, Computer Vision and Pattern Recognition
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Citations in the corpus
- A Survey of Deep Network Solutions for Learning Control in Robotics: From Reinforcement to Imitation (2016, neutral): “strategy for the road segmentation and lane prediction applied on the autonomous vehicles. The success of RNN in object tracking (Ondruska and Posner, 2016) was also leveraged for autonomous driving. Dequaire et al. (2016) predicted future states of the objects based on the current inputs through a ”
- Deep-learning in Mobile Robotics - from Perception to Control Systems: A Survey on Why and Why not (2016, background): “Dequaire et al. (2016) predicted future states of the objects based on the current inputs through a RNN model.”