A Calibration Method of Multi-Camera Pose Based on Monocular Vision

Huang, Dongzhao; Zhao, Qiancheng; Wang, Wenyun; Yang, Tianlong

doi:10.12792/iciae2013.026

Cited by 1 publication

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“…Behavior recognition is performed through the detection and tracking of acquired video sequences, using the related computer vision, image processing and other technologies to describe specific actions and realize behavior recognition. The accuracy of recognition results depends on the extraction of behavior features and the selection of appropriate recognition methods [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. In order to accurately describe the behavior features for subsequent action recognition, different action features are extracted according to the types of the acquired basic data information.…”

Section: Introductionmentioning

confidence: 99%

An Improved Kinect Recognition Method for Identifying Unsafe Behaviors of Metro Passengers

et al. 2022

Sensors

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In order to solve the problem of the low action recognition accuracy of passengers’ unsafe behaviors caused by redundant joints, this study proposes an efficient recognition method based on a Kinect sensor. The method uses the pelvis as the starting point of the vector and high-frequency bone joints as the end point to construct the recognition feature vector. The joint angle difference between actions is obtained by using the cosine law, and the initial test result is converted into action similarity combined with the DTW similarity algorithm. Considering the combination of 3 angle features and 4 joint feature selection methods, 12 combined recognition models are formed. A comparative experiment was carried out to identify five types of unsafe behaviors of metro passengers—punch, one-armed call for help, arms for help, trip forward and trip backwards. The results show that the overall selection of joints has a poor similarity effect and cannot achieve the purpose of recognition. The overall recognition model effect of the local “pelvis divergence method” is higher than that of the local “adjacent joint method”, and the local “pelvis divergence method” has the highest recognition result of the maximum angle difference model, and the recognition results of the five unsafe behaviors are 86.9%, 89.2%, 85.5%, 86.7%, and 88.3%, respectively, and the recognition accuracy of this method is 95.7%, indicating the feasibility of the model. The recognition results are more concentrated and more stable, which significantly improves the recognition rate of metro passengers’ unsafe behavior.

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Section: Introductionmentioning

confidence: 99%

An Improved Kinect Recognition Method for Identifying Unsafe Behaviors of Metro Passengers

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

A Calibration Method of Multi-Camera Pose Based on Monocular Vision

Cited by 1 publication

References 8 publications

An Improved Kinect Recognition Method for Identifying Unsafe Behaviors of Metro Passengers

An Improved Kinect Recognition Method for Identifying Unsafe Behaviors of Metro Passengers

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