A method for object classification in a decision tree based adaptive boosting (AdaBoost) classifier implemented on a single-instruction multiple-data (SIMD) processor is provided that includes receiving feature vectors extracted from N consecutive window positions in an image in a memory coupled to the SIMD processor and evaluating the N consecutive window positions concurrently by the AdaBoost classifier using the feature vectors and vector instructions of the SIMD processor, in which the AdaBoost classifier concurrently traverses decision trees for the N consecutive window positions until classification is complete for the N consecutive window positions.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for object classification in a decision tree based adaptive boosting (AdaBoost) classifier implemented on a single-instruction multiple-data (SIMD) processor, the method comprising: receiving feature vectors extracted from N consecutive window positions in an image in a memory coupled to the SIMD processor, in which N is a vector width of the SIMD processor divided by a bit size of a feature, and in which a feature vector includes N feature values, one feature value for each of the N consecutive window positions; and evaluating the N consecutive window positions concurrently by the AdaBoost classifier using the feature vectors and vector instructions of the SIMD processor, in which the AdaBoost classifier concurrently traverses decision trees for the N consecutive window positions until classification is complete for the N consecutive window positions, in which a decision tree includes a plurality of nodes, a threshold value for each node, and a plurality of leaves, each leaf including a partial score. 2. The method of claim 1, in which evaluating the N consecutive window positions includes: loading a plurality of the feature vectors using a vector load instruction of the SIMD processor, in which one feature vector is loaded for each node of a single decision tree of the AdaBoost classifier; comparing each feature vector to a corresponding threshold vector using a vector compare instruction of the SIMD processor to generate a mask vector for each node, in which the corresponding threshold vector includes N copies of the threshold value for the node corresponding to the feature vector, and in which the mask vector includes N comparison results, one for each of the N features of the feature vector; generating a partial score vector based on the mask vectors and the partial score values of the leaves of the decision tree, the partial score vector including N partial score values, one for each of the N consecutive window positions; accumulating the partial scores into an accumulated score vector, the accumulated score vector including N accumulated score values, one for each of the N consecutive window positions; and comparing the accumulated score vector to an exit threshold vector using a vector compare instruction of the SIMD processor to determine whether or not object classification can be terminated for one or more of the N consecutive window positions. 3. The method of claim 2, in which generating a partial score vector includes: generating a leaf selection mask vector for each of the leaves of the decision tree based on the mask vectors, in which a leaf selection mask vector is a logical combination of mask vectors for nodes in a traversal path of the single decision tree that reaches the leaf corresponding to the leaf selection mask vector; and performing a logical and operation of each leaf selection mask vector with a corresponding leaf vector to select partial scores for each of the N window positions from the leaf vectors, in which a corresponding leaf vector includes N copies of a partial score of the leaf. 4. The method of claim 1, in which the decision trees are two-level binary decision trees. 5. The method of claim 1, in which the AdaBoost classifier is trained for pedestrian classification. 6. The method of claim 1, in which the SIMD processor is a digital signal processor. 7. A digital system comprising: a single-instruction multiple-data (SIMD) processor; a memory component coupled to the SIMD processor, the memory component configured to store features extracted from an image; a plurality of decision trees stored in the memory component, in which each decision tree includes a plurality of nodes, a threshold value for each node, and a plurality of leaves, each leaf including a partial score; and a decision tree based adaptive boosting (AdaBoost) classifier trained for object classification stored in the memory component, the AdaBoost classifier executable on the SIMD processor, in which the AdaBoost classifier uses the plurality of decision trees for object classification, the AdaBoost classifier configured to evaluate N consecutive window positions concurrently using the features and vector instructions of the SIMD processor, in which the AdaBoost classifier concurrently traverses decision trees for the N consecutive window positions until classification is complete for the N consecutive window positions and in which N is a vector width of the SIMD processor divided by a bit size of a feature. 8. The digital system of claim 7, including a feature extraction component coupled to the memory component and configured to extract the features from the N consecutive window positions in an image. 9. The digital system of claim 8, including a camera coupled to the feature extraction component to provide the image. 10. The digital system of claim 7, in which in which the AdaBoost classifier is configured to evaluate the N consecutive window positions by loading a plurality of feature vectors from the memory component using a vector load instruction of the SIMD processor, in which one feature vector is loaded for each node of a single decision tree of the plurality of decision trees and in which a feature vector includes N feature values, one feature value for each of the N consecutive window positions; comparing each feature vector to a corresponding threshold vector using a vector compare instruction of the SIMD processor to generate a mask vector for each node, in which the corresponding threshold vector includes N copies of the threshold value for the node corresponding to the feature vector, and in which the mask vector includes N comparison results, one for each of the N features of the feature vector; generating a partial score vector based on the mask vectors and the partial score values of the leaves of the decision tree, the partial score vector including N partial score values, one for each of the N consecutive window positions; accumulating the partial scores into an accumulated score vector, the accumulated score vector including N accumulated score values, one for each of the N consecutive window positions; and comparing the accumulated score vector to an exit threshold vector using a vector compare instruction of the SIMD processor to determine whether or not object classification can be terminated for one or more of the N consecutive window positions. 11. The digital system of claim 10, in which generating a partial score vector includes: generating a leaf selection mask vector for each of the leaves of the decision tree based on the mask vectors, in which a leaf selection mask vector is a logical combination of mask vectors for nodes in a traversal path of the single decision tree that reaches the leaf corresponding to the leaf selection mask vector; and performing a logical and operation of each leaf selection mask vector with a corresponding leaf vector to select partial scores for each of the N window positions from the leaf vectors, in which a corresponding leaf vector includes N copies of a partial score of the leaf. 12. The digital system of claim 7, in which the decision trees are two level binary decision trees. 13. The digital system of claim 7, in which the AdaBoost classifier is trained for pedestrian classification. 14. The digital system of claim 7, in which the SIMD processor is a digital signal processor. 15. A non-transitory computer readable medium storing software instructions that, when executed on a single-instruction multiple-data (SIMD) processor, cause a method for object classification in a decision tree based adaptive boosting (AdaBoost) classifier to be executed, the method comprising: receiving feature vectors extracted from N consecutive window positions in an image in a memory coupled to the SIMD processor, in which N is a vector width of the SIMD processor divided by a bit size of a feature, and in which a feature vector includes N feature values, one feature value for each of the N consecutive window positions; and evaluating the N consecutive window positions concurrently by the AdaBoost classifier using the feature vectors and vector instructions of the SIMD processor, in which the AdaBoost classifier concurrently traverses decision trees for the N consecutive window positions until classification is complete for the N consecutive window positions, in which a decision tree includes a plurality of nodes, a threshold value for each node, and a plurality of leaves, each leaf including a partial score. 16. The computer readable of claim 15, in which evaluating the N consecutive window positions includes: loading a plurality of the feature vectors using a vector load instruction of the SIMD processor, in which one feature vector is loaded for each node of a single decision tree of the AdaBoost classifier; comparing each feature vector to a corresponding threshold vector using a vector compare instruction of the SIMD processor to generate a mask vector for each node, in which the corresponding threshold vector includes N copies of the threshold value for the node corresponding to the feature vector, and in which the mask vector includes N comparison results, one for each of the N features of the feature vector; generating a partial score vector based on the mask vectors and the partial score values of the leaves of the decision tree, the partial score vector including N partial score values, one for each of the N consecutive window positions; accumulating the partial scores into an accumulated score vector, the accumulated score vector including N accumulated score values, one for each of the N consecutive window positions; and comparing the accumulated score vector to an exit threshold vector using a vector compare instruction of the SIMD processor to determine whether or not object classification can be terminated for one or more of the N consecutive window positions. 17. The computer readable medium of claim 16, in which generating a partial score vector includes: generating a leaf selection mask vector for each of the leaves of the decision tree based on the mask vectors, in which a leaf selection mask vector is a logical combination of mask vectors for nodes in a traversal path of the single decision tree that reaches the leaf corresponding to the leaf selection mask vector; and performing a logical and operation of each leaf selection mask vector with a corresponding leaf vector to select partial scores for each of the N window positions from the leaf vectors, in which a corresponding leaf vector includes N copies of a partial score of the leaf. 18. The computer readable medium of claim 15, in which the decision trees are two-level binary decision trees. 19. The computer readable medium of claim 15, in which the AdaBoost classifier is trained for pedestrian classification. 20. The computer readable medium of claim 15, in which the SIMD processor is a digital signal processor.