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Convolutional Neural Networks for night-time animal orientation estimation | IEEE Conference Publication | IEEE Xplore

Convolutional Neural Networks for night-time animal orientation estimation


Abstract:

In rural areas, wildlife animal road crossings are a threat to both the driver and the wildlife population. Since most accidents take place at night, recent night vision ...Show More

Abstract:

In rural areas, wildlife animal road crossings are a threat to both the driver and the wildlife population. Since most accidents take place at night, recent night vision driver assistance systems are supporting the driver by automatically detecting animals on infrared camera imagery. After detecting an animal on the roadside, the orientation towards the road can give a first cue for an upcoming trajectory prediction. This paper describes an novel classification-based scheme for nighttime animal orientation estimation from single infrared images. Our system classifies already detected animals, in particular deer, as being either oriented left, right or back/front. We propose an approach based on Convolutional Neural Networks which learns multiple stages of invariant features in a supervised end-to-end fashion. Experiments show that our method outperforms baseline methods like HOG/SVM or boosted Haar-stumps on this task.
Date of Conference: 23-26 June 2013
Date Added to IEEE Xplore: 15 October 2013
ISBN Information:
Print ISSN: 1931-0587
Conference Location: Gold Coast, QLD, Australia

References

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