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Pedestrian attribute classification based on label
sensitive convolutional neural network |
Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China |
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Abstract To solve the problem that the classification of pedestrian attributes was affected by the imbalanced pedestrian attributes, a pedestrian attribute classification was proposed based on label sensitive convolutional neural network(CNN). The existing convolutional neural network structure was adjusted. By integrating the positive and negative channel activation modules, the model could perceive more detailed pedestrian attributes. The attribute imbalance loss function was introduced, and the network weights were adaptively updated according to the imbalance ratio of attributes. The back propagation of error was used to increase the weight of smallclass attributes, and the sensitivity of model to the smallclass attributes was improved. 54 attributes were classified on the PETA data set. The results show that compared with MLCNN and other methods, the new method is improved for 36 classification tasks. The average accuracy, the average recall and the average AUC are increased by 2.13%, 2.38% and 1.19%, respectively.
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