Multiperson interaction recognition in images: A body keypoint based feature image analysis

Acharya, Bibhudendra

doi:10.1111/coin.12419

Cited by 8 publications

(5 citation statements)

References 42 publications

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“…They have attained 92.78% accuracy in recognizing human interactions. Verma et al [10] have employed a feature-based neural network to identify human interactions in images. Tang et al [11] devised a novel approach to enhance vision-based safety compliance checks by explicitly categorizing workertool interactions.…”

Section: Related Workmentioning

confidence: 99%

Recognition of Human Interactions in Still Images using AdaptiveDRNet with Multi-level Attention

Dey,

Biswas,

2023

IJACSA

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Human-Human Interaction Recognition (H2HIR) is a multidisciplinary field that combines computer vision, deep learning, and psychology. Its primary objective is to decode and understand the intricacies of human-human interactions. H2HIR holds significant importance across various domains as it enables machines to perceive, comprehend, and respond to human social behaviors, gestures, and communication patterns. This study aims to identify human-human interactions from just one frame, i.e. from an image. Diverging from the realm of video-based interaction recognition, a well-established research domain that relies on the utilization of spatio-temporal information, the complexity of the task escalates significantly when dealing with still images due to the absence of these intrinsic spatio-temporal features. This research introduces a novel deep learning model called AdaptiveDRNet with Multi-level Attention to recognize Human-Human (H2H) interactions. Our proposed method demonstrates outstanding performance on the Human-Human Interaction Image dataset (H2HID), encompassing 4049 meticulously curated images representing fifteen distinct human interactions and on the publicly accessible HII and HIIv2 related benchmark datasets. Notably, our proposed model excels with a validation accuracy of 97.20% in the classification of human-human interaction images, surpassing the performance of EfficientNet, InceptionResNetV2, NASNet Mobile, ConvXNet, ResNet50, and VGG-16 models. H2H interaction recognition's significance lies in its capacity to enhance communication, improve decision-making, and ultimately contribute to the well-being and efficiency of individuals and society as a whole.

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Section: Related Workmentioning

confidence: 99%

Recognition of Human Interactions in Still Images using AdaptiveDRNet with Multi-level Attention

Dey,

Biswas,

2023

IJACSA

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“…In addition to the architectural researches, some action recognition methods [35][36][37][38] attempt to extract more refined motion features with object detection, object tracking, and pose detection methods. The emerging advanced tracking and detection methods [39][40][41] make these possible.…”

Section: Related Workmentioning

confidence: 99%

“…It fuses OpenPose with Kalman filter to track human targets. Verma, Meenpal [37] improved the performance of multiperson interaction recognition by extracting distance and angular relation features based on body keypoints. Based on the pose detection algorithm, Pandurevic, Draga [38] developed a motion sequences analysis method to help in training speed climbing athletes.…”

Section: Related Workmentioning

confidence: 99%

Whole-Body Keypoint and Skeleton Augmented RGB Networks for Video Action Recognition

Guo

Ying

2022

Applied Sciences

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Incorporating multi-modality data is an effective way to improve action recognition performance. Based on this idea, we investigate a new data modality in which Whole-Body Keypoint and Skeleton (WKS) labels are used to capture refined body information. Unlike directly aggregated multi-modality, we leverage distillation to adapt an RGB network to classify action with the feature-extraction ability of the WKS network, which is only fed with RGB clips. Inspired by the success of transformers for vision tasks, we design an architecture that takes advantage of both three-dimensional (3D) convolutional neural networks (CNNs) and the Swin transformer to extract spatiotemporal features, resulting in advanced performance. Furthermore, considering the unequal discrimination among clips of a video, we also present a new method for aggregating the clip-level classification results, further improving the performance. The experimental results demonstrate that our framework achieves advanced accuracy of 93.4% with only RGB input on the UCF-101 dataset.

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“…At present, keypoint detection has been used for human pose recognition [32,33], facial keypoint detection [34,35], human skeleton detection [36], animal pose estimation [37] etc. However, there has been no research into its application to aircraft.…”

Section: Introductionmentioning

confidence: 99%

Research on conflict detection model for taxi‐in process on the apron based on aircraft wingtip keypoint detection

Zhang

Zhu

et al. 2022

IET Intelligent Trans Sys

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Apron safety is critical to aviation operations and aprons are prone to wingtip scraping accidents. However, few studies have been conducted on wingtip conflicts. In this paper, a class of aircraft conflict detection models based on wingtip keypoint detection is devised to solve the problem of wingtip scraping that may occur during the aircraft taxi‐in process on the apron. Firstly, a concise camera calibration technique is designed for the conversion between the pixel coordinate and the airport actual coordinate, which allows for the acquisition of high accuracy activity posture within the apron. Secondly, the static and dynamic safety protection zone delineation methods for wingtip conflict detection are given, taking the apron standard operating procedures and aircraft kinematic model into consideration. Then, the corresponding conflict detection models and algorithms are designed for the possible scraping between the vehicle and aircraft wingtips and between the aircraft and aircraft wingtips during the aircraft taxi‐in process on the apron. Finally, HRNet is selected to detect the keypoint of the wingtip by comparison, and the corresponding models and algorithm validation are carried out based on the apron physical sandbox. The verification results indicate that the models have strong accuracy and real‐time performance.

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Multiperson interaction recognition in images: A body keypoint based feature image analysis

Cited by 8 publications

References 42 publications

Recognition of Human Interactions in Still Images using AdaptiveDRNet with Multi-level Attention

Recognition of Human Interactions in Still Images using AdaptiveDRNet with Multi-level Attention

Whole-Body Keypoint and Skeleton Augmented RGB Networks for Video Action Recognition

Research on conflict detection model for taxi‐in process on the apron based on aircraft wingtip keypoint detection

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