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PARNet: A Joint Loss Function and Dynamic Weights Network for Pedestrian Semantic Attributes Recognition of Smart Surveillance Image

1,2,3, 1, 3, 2,4,*, 3 and 3
1
College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
2
School of Land Science and Technology, China University of Geosciences, Beijing 100083, China
3
Neusoft group co. LTD., Intelligent application division, Intelligent technology research center, Shenyang 110179, China
4
The State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(10), 2027; https://doi.org/10.3390/app9102027
Received: 17 April 2019 / Revised: 13 May 2019 / Accepted: 14 May 2019 / Published: 16 May 2019
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Abstract

The capability for recognizing pedestrian semantic attributes, such as gender, clothes color and other semantic attributes is of practical significance in bank smart surveillance, intelligent transportation and so on. In order to recognize the key multi attributes of pedestrians in indoor and outdoor scenes, this paper proposes a deep network with dynamic weights and joint loss function for pedestrian key attribute recognition. First, a new multi-label and multi-attribute pedestrian dataset, which is named NEU-dataset, is built. Second, we propose a new deep model based on DeepMAR model. The new network develops a loss function, which joins the sigmoid function and the softmax loss to solve the multi-label and multi-attribute problem. Furthermore, the dynamic weight in the loss function is adopted to solve the unbalanced samples problem. The experiment results show that the new attribute recognition method has good generalization performance. View Full-Text
Keywords: pedestrian attributes; surveillance image; semantic attributes recognition; multi-label learning; large-scale database pedestrian attributes; surveillance image; semantic attributes recognition; multi-label learning; large-scale database
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Li, Y.; Tong, G.; Li, X.; Wang, Y.; Zou, B.; Liu, Y. PARNet: A Joint Loss Function and Dynamic Weights Network for Pedestrian Semantic Attributes Recognition of Smart Surveillance Image. Appl. Sci. 2019, 9, 2027.

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