PARNet: A Joint Loss Function and Dynamic Weights Network for Pedestrian Semantic Attributes Recognition of Smart Surveillance Image
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Text and Document Classification Technologies · Web Data Mining and Analysis · Advanced Image and Video Retrieval Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
School of Land Science and Technology, China University of Geosciences, Beijing 100083, China
Neusoft group co. LTD., Intelligent application division, Intelligent technology research center, Shenyang 110179, China
The State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China
Funding
National Natural Science Foundation of China · National High Technology Research and Development Program of China
References (25)
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