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Person Re-identification algorithm Based on the Fusion of Deep Feature and lomo Feature 11

Person Re-identification Algorithm Based on the Fusion of De...

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11th International Conference on Digital Image Processing (ICDIP)

作者： Liang, Zi Chi, Jianning Hua, Ruzhao Zhang, Yue Wang, Wenhao Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Northeastern Univ Fac Robot Sci & Engn Shenyang 110819 Peoples R China

ISBN: (数字)9781510630765

ISBN: (纸本)9781510630765

Person Re-identification is a sub-problem of image retrieval, using computer vision techniques to judge whether a certain identical pedestrian exists among different images or video sequences, which has attracted more and more attention of researchers. In this paper, regarding the fact that under non-overlapping multi-camera, traditional handcrafted features have a limited presentation power in re-identifying the pedestrians and that deep features have complicated parameters while training. A re-identification method based on the deep fusion of handcrafted features and deep features was proposed, which cut down the number of parameters but still guaranteed the accuracy, achieving the advancement of both precision and capacity. In our model, the lomo algorithm is used to extract the handcrafted features from the images first. Then, the dimensionality of those features are reduced by Guassian Pooling for efficiency. After that, they are connected to the deep fusion network with the deep features extracted from the same images by a modification of ResNet50. Finally, the fused features are sent to the classifier for the re-identification. In the training process, we proposed a training strategy called Gradient Freezing after studying the training details in the application of transfer learning on neural network. Experiments have proved that the accuracy of applying the deep fusion network that fused with deep features and handcrafted features is 30% higher than that of the ResNet50 alone, and that the time it consumes is reduced by 10 epoches through the gradient freezing method. Moreover, several experiments carried out on dataset Marketl501 indicate that under Single Query on Marketl501, Rankl(the probability of matching successfully for the first time) can reach a high number of 81.74% and mAP(mean average Precision) of 68.75%.

关键词： Person search lomo algorithm Residual neural network Feature fusion network

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Person Re-identification algorithm Based on the Fusion of Deep Feature and lomo Feature

Person Re-identification Algorithm Based on the Fusion of De...

引用

2019第十一届数字图像处理国际会议

作者： Zi Liang Jianning Chi Ruzhao Hua Yue Zhang Wenhao Wang College of Information Science and Engineering Northeastern University Faculty of Robot Science and Engineering Northeastern University

Person Re-identification is a sub-problem of image retrieval,using computer vision techniques to judge whether a certain identical pedestrian exists among different images or video sequences,which has attracted more and more attention of *** this paper,regarding the fact that under non-overlapping multi-camera,traditional handcrafted features have a limited presentation power in re-identifying the pedestrians and that deep features have complicated parameters while training.A re-identification method based on the deep fusion of handcrafted features and deep features was proposed,which cut down the number of parameters but still guaranteed the accuracy,achieving the advancement of both precision and *** our model,the lomo algorithm is used to extract the handcrafted features from the images ***,the dimensionality of those features are reduced by Guassian Pooling for *** that,they are connected to the deep fusion network with the deep features extracted from the same images by a modification of ***,the fused features are sent to the classifier for the *** the training process,we proposed a training strategy called Gradient Freezing after studying the training details in the application of transfer learning on neural *** have proved that the accuracy of applying the deep fusion network that fused with deep features and handcrafted features is 30% higher than that of the ResNet50 alone,and that the time it consumes is reduced by 10 epoches through the gradient freezing ***,several experiments carried out on dataset Marketl501 indicate that under Single Query on Marketl501,Rankl（the probability of matching successfully for the first time） can reach a high number of 81.74% and mAP（mean average Precision） of 68.75%.

关键词： Person search lomo algorithm Residual neural network Feature fusion network

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