Action Recognition Framework in Traffic Scene for Autonomous Driving System

Xu, Feiyi; Xu, Feng; Xie, Jiucheng; Pun, Chi‐Man; Lu, Huimin; Gao, Hao

doi:10.1109/tits.2021.3135251

Cited by 24 publications

(4 citation statements)

References 29 publications

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“…Gesture Recognition with Camera Data Gesture recognition is applied to react to humans outside the vehicle [40,22] and to the passenger's desires [8,36]. For gesture recognition of police officers, related work processes camera image snippets [3] directly.…”

Section: Related Workmentioning

confidence: 99%

“…For processing skeletons to predict the gesture, recurrent neural networks [35] or convolutional neural networks [22] are applied. Besides the police officer gesture recognition, the actions of other human traffic participants like cyclists [40] or pedestrians [6] are also analyzed in literature. Similar to pedestrian gesture recognition is the pedestrian intention prediction [25,1], where the pedestrian's intention to cross the street should be recognized.…”

Section: Related Workmentioning

confidence: 99%

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Gesture Recognition with Keypoint and Radar Stream Fusion for Automated Vehicles

Holzbock,

Kern,

Waldschmidt

et al. 2023

Preprint

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We present a joint camera and radar approach to enable autonomous vehicles to understand and react to human gestures in everyday traffic. Initially, we process the radar data with a PointNet followed by a spatio-temporal multilayer perceptron (stMLP). Independently, the human body pose is extracted from the camera frame and processed with a separate stMLP network. We propose a fusion neural network for both modalities, including an auxiliary loss for each modality. In our experiments with a collected dataset, we show the advantages of gesture recognition with two modalities. Motivated by adverse weather conditions, we also demonstrate promising performance when one of the sensors lacks functionality.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Gesture Recognition with Keypoint and Radar Stream Fusion for Automated Vehicles

Holzbock,

Kern,

Waldschmidt

et al. 2023

Preprint

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show abstract

“…D ENSEPOSE [1] is a fundamental task for human understanding, which is a cornerstone for advancements in intelligent transportation systems [2]- [4]. It facilitates many three-dimensional visual applications, including but not limited to texture/pose transfer [5], [6], 3D human reconstruction [7], and human image animation [8].…”

Section: Introductionmentioning

confidence: 99%

GC RCNN: Learning Interdependent IUV for Geodesic Consistent DensePose

Sun,

Liu

2024

Preprint

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“…Human activity recognition (HAR) involves identifying various human behaviors through a series of observations of individuals and their surrounding environment [1]. HAR has been generally applied in many fields, such as security and surveillance [2], sports and fitness [3], industry and manufacturing [4], autonomous driving [5], and the references therein.…”

Section: Introduction and Related Workmentioning

confidence: 99%

A Multi-Layer Classifier Model XR-KS of Human Activity Recognition for the Problem of Similar Human Activity

Tan,

Qin,

Tang

et al. 2023

Sensors

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Sensor-based human activity recognition is now well developed, but there are still many challenges, such as insufficient accuracy in the identification of similar activities. To overcome this issue, we collect data during similar human activities using three-axis acceleration and gyroscope sensors. We developed a model capable of classifying similar activities of human behavior, and the effectiveness and generalization capabilities of this model are evaluated. Based on the standardization and normalization of data, we consider the inherent similarities of human activity behaviors by introducing the multi-layer classifier model. The first layer of the proposed model is a random forest model based on the XGBoost feature selection algorithm. In the second layer of this model, similar human activities are extracted by applying the kernel Fisher discriminant analysis (KFDA) with feature mapping. Then, the support vector machine (SVM) model is applied to classify similar human activities. Our model is experimentally evaluated, and it is also applied to four benchmark datasets: UCI DSA, UCI HAR, WISDM, and IM-WSHA. The experimental results demonstrate that the proposed approach achieves recognition accuracies of 97.69%, 97.92%, 98.12%, and 90.6%, indicating excellent recognition performance. Additionally, we performed K-fold cross-validation on the random forest model and utilized ROC curves for the SVM classifier to assess the model’s generalization ability. The results indicate that our multi-layer classifier model exhibits robust generalization capabilities.

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Action Recognition Framework in Traffic Scene for Autonomous Driving System

Cited by 24 publications

References 29 publications

Gesture Recognition with Keypoint and Radar Stream Fusion for Automated Vehicles

Gesture Recognition with Keypoint and Radar Stream Fusion for Automated Vehicles

GC RCNN: Learning Interdependent IUV for Geodesic Consistent DensePose

A Multi-Layer Classifier Model XR-KS of Human Activity Recognition for the Problem of Similar Human Activity

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