Using 20 past frames as temporal context improves soccer ball detection recall by about 1.5 points over a single-frame detector, with a temporal convolutional network giving the fastest inference.
Deep Learning for Semantic Segmentation on Minimal Hardware
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abstract
Deep learning has revolutionised many fields, but it is still challenging to transfer its success to small mobile robots with minimal hardware. Specifically, some work has been done to this effect in the RoboCup humanoid football domain, but results that are performant and efficient and still generally applicable outside of this domain are lacking. We propose an approach conceptually different from those taken previously. It is based on semantic segmentation and does achieve these desired properties. In detail, it is being able to process full VGA images in real-time on a low-power mobile processor. It can further handle multiple image dimensions without retraining, it does not require specific domain knowledge for achieving a high frame rate and it is applicable on a minimal mobile hardware.
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Utilizing Temporal Information in Deep Convolutional Network for Efficient Soccer Ball Detection and Tracking
Using 20 past frames as temporal context improves soccer ball detection recall by about 1.5 points over a single-frame detector, with a temporal convolutional network giving the fastest inference.