Determining distance of objects
A computer implemented method for determining a distance of an object comprises the following steps carried out by computer hardware components: determining an image containing the object; determining a class of the object based on the image; determining a coarse estimation of the distance based on a distance sensor; and determining the distance of the object based on the coarse estimation and based on the class of the object.
1. A computer-implemented method comprising:
determining an image that contains an object;
determining a class of the object based on the image;
determining a bounding box of the object in the image;
determining a coarse estimation of a distance to the object based on matching a measurement of a distance sensor with the bounding box; and
determining the distance to the object based on the coarse estimation and based on the class of the object.
2. The computer-implemented method of claim 1 , wherein the class of the object is selected from a plurality of traffic-participant types.
3. The computer-implemented method of claim 2 , wherein the plurality of traffic-participant types includes at least two of a pedestrian, a bicycle, a car, or a truck.
4. The computer-implemented method of claim 1 , wherein the class of the object is selected from a plurality of orientations.
5. The computer-implemented method of claim 4 , wherein the plurality of orientations includes at least two of left, upper-left, upper, upper right, right, bottom-right, bottom, or bottom-left.
6. The computer-implemented method of claim 1 , wherein the class is determined using an artificial neural network.
7. The computer-implemented method of claim 6 , wherein the artificial neural network is of a single shot detector (SSD) type.
8. The computer-implemented method of claim 1 , wherein the distance is determined based on a hash table for the class of the object.
9. The computer-implemented method of claim 8 , wherein the hash table comprises a correction for determining the distance based on the coarse estimation.
10. A computer system comprising a processor configured to:
determine an image that contains an object;
determine a class of the object based on the image;
determine a bounding box of the object in the image;
determine a coarse estimation of a distance to the object based on matching a measurement of a distance sensor with the bounding box; and
determine the distance to the object based on the coarse estimation and based on the class of the object.
11. The computer system of claim 10 , wherein the class of the object is selected from a plurality of traffic-participant types.
12. The computer system of claim 11 , wherein the plurality of traffic-participant types includes at least two of a pedestrian, a bicycle, a car, or a truck.
13. The computer system of claim 10 , wherein the class of the object is selected from a plurality of orientations.
14. The computer system of claim 13 , wherein the plurality of orientations includes at least two of left, upper-left, upper, upper right, right, bottom-right, bottom, or bottom-left.
15. The computer system of claim 10 , wherein the class is determined using an artificial neural network.
16. The computer system of claim 10 , wherein the computer system is installed in a vehicle that includes the distance sensor and a camera configured to acquire the image.
17. A non-transitory computer readable medium comprising instructions that, when executed, cause a processor to:
determine an image that contains an object;
determine a class of the object based on the image;
determine a bounding box of the object in the image;
determine a coarse estimation of a distance to the object based on matching a measurement of a distance sensor with the bounding box; and
determine the distance to the object based on the coarse estimation and based on the class of the object.