Detecting a Moving Object in the Passenger Compartment of a Vehicle

Abstract

Disclosed are computer implemented methods for detecting a moving object in the passenger compartment of a vehicle. In an aspect, the method includes illuminating the inside of the passenger compartment of the vehicle using an infrared light source, obtaining a stream of a plurality of consecutive images from the inside of the illuminated passenger compartment of the vehicle using an infrared camera, identifying moving objects in the stream based on an object detection algorithm using a processor, and improving the image quality of the moving objects in the stream based on a machine-learning algorithm using the processor.

Claims

1. A computer implemented method, the method comprising: illuminating an inside of a passenger compartment of a vehicle using an infrared light source; obtaining a stream of a plurality of consecutive images from the illuminated inside of the passenger compartment of the vehicle using an infrared camera; identifying moving objects in the stream based on an object detection algorithm using a processor; and improving an image quality of the identified moving objects in the stream based on a machine-learning algorithm using the processor.

2. The computer implemented method according to claim 1, the method further comprising: identifying static objects in the stream based on the object detection algorithm using the processor; and improving the image quality of the static objects in the stream based on an image stacking algorithm using the processor.

3. The computer implemented method according to claim 2, the method further comprising: classifying moving objects in the stream based on a neural network using the processor.

4. The computer implemented method according to claim 3, the method further comprising: providing a notification based on the classification of the moving objects using the processor.

5. The computer implemented method according to claim 4, the method further comprising: classifying static objects in the stream based on a neural network using the processor.

6. The computer implemented method according to claim 4, the method further comprising: taking an action based on the classification of the moving objects using the processor.

7. The computer implemented method according to claim 3, the method further comprising: taking an action based on the classification of the moving objects using the processor.

8. The computer implemented method according to claim 7, the method further comprising: classifying static objects in the stream based on a neural network using the processor.

9. The computer implemented method according to claim 3, the method further comprising: classifying static objects in the stream based on a neural network using the processor.

10. The computer implemented method according to claim 2, the method further comprising: classifying static objects in the stream based on a neural network using the processor.

11. The computer implemented method according to claim 10, the method further comprising: providing a notification based on the classification of the static objects using the processor.

12. The computer implemented method according to claim 11, the method further comprising: taking an action based on the classification of the static objects using the processor.

13. The computer implemented method according to claim 10, the method further comprising: taking an action based on the classification of the static objects using the processor.

14. The computer implemented method according to claim 1, wherein the object detection algorithm is a semantic segmentation algorithm.

15. The computer implemented method according to claim 1, the method further comprising: classifying moving objects in the stream based on a neural network using the processor.

16. The computer implemented method according to claim 1, the method further comprising: identifying static objects in the stream based on the object detection algorithm using the processor; improving an image quality of the static objects in the stream based on an image stacking algorithm using the processor; classifying moving objects in the stream based on a neural network using the processor; providing a notification based on the classification of the moving objects using the processor; taking an action based on the classification of the moving objects using the processor; and classifying static objects in the stream based on a neural network using the processor.

17. The computer implemented method according to claim 16, the method further comprising: providing a notification based on the classification of the static objects using the processor.

18. The computer implemented method according to claim 17, the method further comprising: taking an action based on the classification of the static objects using the processor.

19. A system comprising at least one processor configured to: illuminate an inside of a passenger compartment of a vehicle using an infrared light source; obtain a stream of a plurality of consecutive images from the illuminated inside of the passenger compartment of the vehicle using an infrared camera; identify moving objects in the stream based on an object detection algorithm using a processor; and improve an image quality of the identified moving objects in the stream based on a machine-learning algorithm using the processor.

20. A non-transitory computer readable medium comprising instructions that, when executed, configure at least one processor to: illuminate an inside of a passenger compartment of a vehicle using an infrared light source; obtain a stream of a plurality of consecutive images from the illuminated inside of the passenger compartment of the vehicle using an infrared camera; identify moving objects in the stream based on an object detection algorithm using a processor; and improve an image quality of the identified moving objects in the stream based on a machine-learning algorithm using the processor.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Example embodiments and functions of the present disclosure are described herein in conjunction with the following drawings, showing schematically:

[0040] FIG. 1 a top view of a computer system for detecting a moving object in the passenger compartment of a vehicle according to an embodiment; and

[0041] FIG. 2 a flow chart of a method for detecting a moving object in the passenger compartment of a vehicle according to an embodiment.

DETAILED DESCRIPTION

[0042] The present disclosure relates to methods and systems for detecting a person in the passenger compartment of a vehicle.

[0043] FIG. 1 depicts a top view of a computer system 10 for detecting a moving object in the passenger compartment of a vehicle according to an embodiment.

[0044] Therein, the computer system 10 comprises a processor 11, an infrared light source unit comprising at least one infrared light source 12 and an infrared camera unit comprising at least one infrared camera 13.

[0045] Therein, the computer system 10 is adapted to illuminate the inside of the passenger compartment of the vehicle using the infrared light source 12.

[0046] The computer system 10 is further adapted to obtain a stream of a plurality of consecutive images from the inside of the illuminated passenger compartment of the vehicle using the infrared camera 13.

[0047] The computer system 10 is further adapted to identify moving objects in the stream based on an object detection algorithm using the processor 11.

[0048] The computer system 10 is further adapted to improve the image quality of the moving objects in the stream based on a machine-learning algorithm using the processor 11.

[0049] The computer system 10 is further adapted to identify static objects in the stream based on the object detection algorithm using the processor 11.

[0050] The computer system 10 is further adapted to improve the image quality of the static objects in the stream based on an image stacking algorithm using the processor 11.

[0051] The computer system 10 is further adapted to classify moving objects in the stream based on a neural network using the processor 11.

[0052] The computer system 10 is further adapted to provide a notification based on the classification using the processor 11.

[0053] The computer system 10 is further adapted to classify static objects in the stream based on a neural network using the processor 11.

[0054] The computer system 10 is further adapted to provide a notification based on the classification using the processor 11.

[0055] The computer system 10 is further adapted to take an action based on the classification using the processor 11.

[0056] FIG. 2 depicts a flow chart of a method 100 for detecting a moving object in the passenger compartment of a vehicle according to an embodiment.

[0057] The method 100 comprises, in a first step 110, to illuminate the inside of the passenger compartment of the vehicle.

[0058] The method 100 comprises, in a further step 120, to obtain a stream of a plurality of consecutive images from the inside of the illuminated passenger compartment of the vehicle.

[0059] The method 100 comprises, in a further step 130, to identify moving objects in the stream based on an object detection algorithm.

[0060] The method 100 comprises, in a further step 140, to improve the image quality of the moving objects in the stream based on a machine-learning algorithm.

[0061] The method 100 comprises, in a further step 150, to identify static objects in the stream based on the object detection algorithm.

[0062] The method 100 comprises, in a further step 160, to improving the image quality of the static objects in the stream based on an image stacking algorithm.

[0063] The method 100 comprises, in a further step 170, to classifying static and moving objects in the stream based on a neural network.

[0064] The method 100 comprises, in a further step 180, to provide a notification and/or take an action based on the classification of the static and moving objects.

[0065] After the final step, the method 100 may return to step 110 and repeat itself.

[0066] The use of “example,” “advantageous,” and grammatically related terms means “serving as an example, instance, or illustration,” and not “preferred” or “advantageous over other examples.” Items represented in the accompanying figures and terms discussed herein may be indicative of one or more items or terms, and thus reference may be made interchangeably to single or plural forms of the items and terms in this written description. The use herein of the word “or” may be considered use of an “inclusive or,” or a term that permits inclusion or application of one or more items that are linked by the word “or” (e.g., a phrase “A or B” may be interpreted as permitting just “A,” as permitting just “B,” or as permitting both “A” and “B”), unless the context clearly dictates otherwise. Also, as used herein, a phrase referring to “at least one of” a list of items refers to any combination of those items, including single members. For instance, “at least one of a, b, or c” can cover a, b, c, a-b, a-c, b-c, and a-b-c, as well as any combination with multiples of the same element (e.g., a-a, a-a-a, a-a-b, a-a-c, a-b-b, a-c-c, b-b, b-b-b, b-b-c, c-c, c-c-c, or any other ordering of a, b, and c).

REFERENCE NUMERAL LIST

[0067] 10 computer system [0068] 11 processor [0069] 12 infrared light source [0070] 13 infrared camera [0071] 100 method [0072] 110 method step [0073] 120 method step [0074] 130 method step [0075] 140 method step [0076] 150 method step [0077] 160 method step [0078] 170 method step [0079] 180 method step