Real-time multiclass driver action recognition using random forests
First Claim
1. A method for performing action recognition on an image of a driver in a vehicle, the method comprising:
- receiving, by a computing system, an image of the driver in the vehicle;
accessing a random forest model comprising a plurality of decision trees;
generating a plurality of predictions of the action being performed by the driver in the image through the random forest model, each prediction generated by one of the plurality of decision trees, each of the plurality of predictions comprising a predicted driver action and a confidence score comprising a ratio or percentage;
grouping the plurality of generated predictions into a plurality of groups by the predicted driver action, such that each group of the plurality of groups is associated with a single predicted driver action;
combining the confidence scores of the generated predictions for each group to determine a single combined score for each group relating to the predicted driver action associated with each group; and
selecting the driver action associated with a highest combined confidence score from the plurality of groups.
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Abstract
An action recognition system recognizes driver actions by using a random forest model to classify images of the driver. A plurality of predictions is generated using the random forest model. Each prediction is generated by one of the plurality of decision trees and each prediction comprises a predicted driver action and a confidence score. The plurality of predictions is regrouped into a plurality of groups with each of the plurality of groups associated with one of the driver actions. The confidence scores are combined within each group to determine a combined score associated with each group. The driver action associated with the highest combined score is selected.
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Citations
20 Claims
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1. A method for performing action recognition on an image of a driver in a vehicle, the method comprising:
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receiving, by a computing system, an image of the driver in the vehicle; accessing a random forest model comprising a plurality of decision trees; generating a plurality of predictions of the action being performed by the driver in the image through the random forest model, each prediction generated by one of the plurality of decision trees, each of the plurality of predictions comprising a predicted driver action and a confidence score comprising a ratio or percentage; grouping the plurality of generated predictions into a plurality of groups by the predicted driver action, such that each group of the plurality of groups is associated with a single predicted driver action; combining the confidence scores of the generated predictions for each group to determine a single combined score for each group relating to the predicted driver action associated with each group; and selecting the driver action associated with a highest combined confidence score from the plurality of groups. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A non-transitory computer-readable storage medium storing instructions for performing action recognition on an image of a driver in a vehicle, the instructions when executed by a processor causing the processor to perform steps including:
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receiving, by a computing system, an image of the driver in the vehicle; accessing a random forest model comprising a plurality of decision trees; generating a plurality of predictions through the random forest model, each prediction generated by one of the plurality of decision trees, each of the plurality of predictions comprising a predicted driver action and a confidence score comprising a ratio or percentage; grouping the plurality of generated predictions into a plurality of groups by the predicted driver action, such that each group of the plurality of groups is associated with a single predicted driver action; combining the confidence scores of the generated predictions for each group to determine a single combined score for each group relating to the predicted driver action associated with each group; and selecting the driver action associated with a highest combined confidence score from the plurality of groups. - View Dependent Claims (11, 12, 13, 14, 15)
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16. A method for learning a random forest model for action recognition, the random forest model comprising a plurality of decision trees, the method comprising:
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receiving, by a computing system, a plurality of training images, each training image depicting a driver action being performed inside a vehicle and each training image having a label identifying the driver action being performed; generating a test corresponding to a parent node of one of the plurality of decision trees, the test comprising one or more test parameters; applying the test to each training image to classify each training image into a plurality of image groups including at least a first image group and a second image group;
determining if an entropy value of the first image group is below a threshold value;responsive to a determination that the entropy value of the first image group is below the threshold value, generating a prediction based on the labels associated with the first image group, the prediction comprising a driver action and a confidence score comprising a ratio or percentage, and generating a leaf node associated with the prediction as a child node of the parent node; and responsive to determining that the entropy value of the first image group is not below the threshold value, generating a branch node associated with the first image group as a child node of the parent node;
whereinthe generated prediction is grouped into one of a plurality of groups by the prediction, such that each group of the plurality of groups is associated with a single prediction. - View Dependent Claims (17, 18, 19, 20)
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Specification