Convolutional Neural Networks for night-time animal orientation estimation

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.