Method for detecting and re-identifying objects using a neural network
AssigneeRobert Bosch GmbH
InventorsDenis Stalz-John, Tamas Kapelner
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Abstract
A method for detecting and re-identifying objects using a neural network. The method includes the steps: extracting features from an image, the features comprising information about at least one object in the image; detecting the at least one object in the image using an anchor-based object detection based on the extracted features, classification data being determined by a classification for detecting the object with the aid of at least one anchor and regression data being determined by a regression; and re-identifying the at least one object by determining embedding data based on the extracted features, the embedding data representing an object description for the at least one feature of the image.
Claims10
A method using a neural network, the method comprising the following steps:
- extracting features from each of a plurality of images, the features including information about objects in the images;
- detecting the objects in the images using an anchor-based object detection based on the extracted features, the detecting including:
- determining classification data by a classification of the objects using at least one anchor;
- determining regression data by a regression;
- and
- determining, respectively for each of the images, a respective embedding data vector that describes one of the objects based on the extracted features;
- and
- for each respective one of at least one of the objects, tracking the respective object between different ones of the images based on a degree of similarity between the embedding data vectors respectively determined for the different ones of the images, the tracking including identifying that the object identified as being in the different ones of the images is the same.
A neural network, comprising:
- a feature extractor configured to extract features from each of a plurality of images, the features including information about objects in the images;
- a classification layer configured to determine classification data for detecting the objects in the images using an anchor-based object detection;
- a regression layer configured to determine regression data for detecting the objects in the images using the anchor-based object detection;
- and
- an embedding layer configured to determine, respectively for each of the images, a respective embedding data vector that describes one of the objects based on the extracted features;
- wherein the neural network is configured to, for each respective one of at least one of the objects, track the respective object between different ones of the images based on a degree of similarity between the embedding data vectors respectively determined for the different ones of the images, the tracking including identifying that the object identified as being in the different ones of the images is the same.
A control method for an at least partially autonomous robot, the method comprising the following steps:
- receiving image data of the at least partially autonomous robot, the image data including a plurality of images representing a surroundings of the robot;
- applying a neural network method using a neural network, the neural network method including:
- extracting features from each of the plurality of images, the features including information about objects in the images;
- detecting the objects in the images using an anchor-based object detection based on the extracted features, the detecting including:
- determining classification data by a classification of the objects using at least one anchor;
- determining regression data by a regression;
- and
- determining, respectively for each of the images, a respective embedding data vector that describes one of the objects based on the extracted features;
- and
- for each respective one of at least one of the objects, tracking the respective object between different ones of the images based on a degree of similarity between the embedding data vectors respectively determined for the different ones of the images, the tracking including identifying that the object identified as being in the different ones of the images is the same;
- and
- controlling the at least partially autonomous robot based on the tracking.
A non-transitory machine-readable storage medium on which is stored a computer program that is executable by a computer, and that, when executed, causes the computer to perform a method using a neural network, the method comprising the following steps:
- extracting features from each of a plurality of images, the features including information about objects in the images;
- detecting the objects in the images using an anchor-based object detection based on the extracted features, the detecting including:
- determining classification data by a classification of the objects using at least one anchor;
- and
- determining regression data by a regression;
- and
- determining, respectively for each of the images, a respective embedding data vector that describes one of the objects based on the extracted features;
- and
- for each respective one of at least one of the objects, tracking the respective object between different ones of the images based on a degree of similarity between the embedding data vectors respectively determined for the different ones of the images, the tracking including identifying that the object identified as being in the different ones of the images is the same.
Dependencies are inferred from the claim text.