Identification method of fine-grained attributes of pedestrians under complex scenes

Abstract

The invention discloses an identification method of fine-grained attributes of pedestrians under complex scenes. A classification model is adopted for fine-grained attribute identification on sub-components of a detected pedestrian; association analysis is respectively carried out on identified attributes and a pedestrian gender, and attributes with high correlation are selected for multi-task learning; then convolutional neural network (CNN) models constructed by multi-task learning are trained, and results of the convolutional neural network models with a highest identification rate are selected for the multiple attributes to use the same as a final result; and finally, a gender attribute of the pedestrian is judged according to a customized decision function. The method can realize overall-to-local detection of the pedestrians in the complex scenes, realize more accurate detection and identification of the attributes of pedestrian sub-components, and avoid interference of backgrounds and other information, also solves, at the same time, the problem that detection correctness rates of models on small objects are low, and has higher information accuracy.

Description

technical field [0001] The invention belongs to the technical field of pattern recognition and machine vision, and relates to target detection and recognition technology, in particular to a detection and recognition method for fine-grained attributes of pedestrians in complex scenes. Background technique [0002] In recent years, with the development of pattern recognition and machine vision, target detection and recognition technology has been greatly developed, and a large number of applications have been realized in the fields of video scene monitoring, robot control, intelligent transportation, and driverless cars. [0003] The method of target detection and recognition is mainly divided into two steps, the first step is target detection, and the second step is image classification. Traditional object detection methods mainly use sliding window + hand-designed features. The main methods are feature descriptor-based, shape feature-based and edge-based object detection; tr...

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Problems solved by technology

[0010] In order to overcome the deficiencies of the prior art above, the present invention provides a detection and recognition method for fine-grained attributes of pedestrians in complex scenes (Fusion of Convolutional Neural Networks Based On MultitaskLearning for Recognition of Pedestrian Attribute, FMRPA), by fusing multi-attribute The Convolutional Neural Network (CNN) model of multi-task learning can realize more accurate detection and recognition of pedestrian sub-component attributes, and can solve the problems of low precision and poor timeliness existing in existing methods, and has high recognition accuracy precision

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