Device, system and method for object recognition

A system for object recognition, said system comprising:

• a depth camera for acquiring a depth image of a scene, the depth image comprising depth information representing the distance between the depth camera and elements of the scene depicted in the depth image;
• and
• a device for object recognition based on the acquired depth image, said device comprising:
• an input for obtaining a depth image of a scene via a depth camera, the depth image comprising depth information representing a distance between the depth camera and elements of the scene depicted in the depth image,
• a processor for:
• computing, from the depth image,
• a noise variance map by computing pixel noise variances at object boundaries of one or more objects in the depth image,
• a depth confidence map by filtering depth value based on their distance to the depth camera, and
• a motion confidence map by filtering out variances caused by motion of a person in the scene,
• computing, from the noise variance map, the depth confidence map and the motion confidence map, one or more candidate regions and their confidence in the depth image, a candidate region being a region potentially representing an object or a part of the object, and
• selecting the one or more candidate regions baying the highest confidence as final region of interest representing the object to be recognized;
• and
• an output for generating a representation of a final region of interest (ROT) to be utilized by an end user.

A method for object recognition, said method comprising:

• obtaining a depth image of a scene, the depth image comprising depth information representing the distance between a depth camera and elements of the scene depicted in the depth image,
• computing, from the depth image,
• a noise variance map by computing pixel noise variances at object boundaries of one or more objects in the depth image,
• a depth confidence map by filtering depth values based on their distance to the depth camera, and
• a motion confidence map by filtering out variances caused by motion of a person in the scene,
• computing, from the noise variance map, the depth confidence map and the motion confidence map, one or more candidate regions and their confidence in the depth image, a candidate region being a region potentially representing an object or a part of the object, and
• selecting the one or more candidate regions having the highest confidence as final region of interest representing the object to be recognized.