Skeleton model based behavior recognition for pedestrians and cyclists from vehicle sce ne camera

Deng, Qiwen; Tian, Renran; Chen, Yaobin; Li, Kang

doi:10.1109/ivs.2018.8500359

Cited by 8 publications

(3 citation statements)

References 27 publications

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“…It expanded the matrix by adding -1 to previous frames and collecting data from the current frame if the candidates exceeded the maximum number. If one individual vanishes from a video, a penalty value must be added to ensure no data is recorded later [23].…”

Section: Pose Trackingmentioning

confidence: 99%

Human Fall Down Recognition Using Coordinates Key Points Skeleton

Ali¹,

Hussain²,

Sadiq³

2022

Int. J. Onl. Eng.

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Falls pose a substantial threat to human safety and can quickly result in disastrous repercussions. This threat is particularly true for the elderly, where falls are the leading cause of hospitalization and injury-related death. A fall that is detected and responded to quickly has a lower danger and long-term impact. Many real-time fall detection solutions are available; however, these solutions have specific privacy, maintenance, and proper use issues. Vision-based fall event detection has the benefit of being completely private and straightforward to use and maintain. However, in real-world scenarios, falls are diverse and result in high detection instability. This study proposes a novel vision-based technique for fall detection and analyzes an extracted skeleton to define human postures. OpenPose can be used to get skeletal information about the human body. It identifies a fall using three critical parameters: the center of the value of the head and shoulder coordinates, the critical points of the shoulder coordinates, and the distance between the center of the skeleton's head and the floor with the angle between the center of the shoulders and the ground. Our proposed methodology was effective, with a classification accuracy of 97.7%.

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Section: Pose Trackingmentioning

confidence: 99%

Human Fall Down Recognition Using Coordinates Key Points Skeleton

Ali¹,

Hussain²,

Sadiq³

2022

Int. J. Onl. Eng.

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“…As an aside note, even we have not included in this paper, we also experimented with LSTMs but the obtained results were not better than the ones that we will report in this paper. In [28], the main focus is to propose a human pose extraction method, the further analysis of how to use it for automatic action recognition is not considered, only a manually guided visual analysis is performed and, therefore, no quantitative results are reported.…”

Section: Related Workmentioning

confidence: 99%

Intention Recognition of Pedestrians and Cyclists by 2D Pose Estimation

Fang

López

2020

IEEE Trans. Intell. Transport. Syst.

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Anticipating the intentions of vulnerable road users (VRUs) such as pedestrians and cyclists is critical for performing safe and comfortable driving maneuvers. This is the case for human driving and, thus, should be taken into account by systems providing any level of driving assistance, from advanced driver assistant systems (ADAS) to fully autonomous vehicles (AVs). In this paper, we show how the latest advances on monocular vision-based human pose estimation, i.e. those relying on deep Convolutional Neural Networks (CNNs), enable to recognize the intentions of such VRUs. In the case of cyclists, we assume that they follow traffic rules to indicate future maneuvers with arm signals. In the case of pedestrians, no indications can be assumed. Instead, we hypothesize that the walking pattern of a pedestrian allows to determine if he/she has the intention of crossing the road in the path of the ego-vehicle, so that the ego-vehicle must maneuver accordingly (e.g. slowing down or stopping). In this paper, we show how the same methodology can be used for recognizing pedestrians and cyclists' intentions. For pedestrians, we perform experiments on the JAAD dataset. For cyclists, we did not found an analogous dataset, thus, we created our own one by acquiring and annotating videos which we share with the research community. Overall, the proposed pipeline provides new state-of-the-art results on the intention recognition of VRUs.

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“…This can be relevant for example for intention recognition of pedestrians, where movements of limbs or head are also described (compare e.g. [42]).…”

Section: Categorization and Evaluationmentioning

confidence: 99%

From Traffic Sensor Data To Semantic Traffic Descriptions: The Test Area Autonomous Driving Baden-Württemberg Dataset (TAF-BW Dataset)

Zipfl

Fleck

Zofka

et al. 2020

2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)

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The verification and validation of autonomous driving vehicles remains a major challenge due to the high complexity of autonomous driving functions. Scenario-based testing is a promising method for validating such a complex system. Ontologies can be utilized to produce test scenarios that are both meaningful and relevant. One crucial aspect of this process is selecting the appropriate method for describing the entities involved. The level of detail and specific entity classes required will vary depending on the system being tested. It is important to choose an ontology that properly reflects these needs.This paper summarizes key representative ontologies for scenario-based testing and related use cases in the field of autonomous driving. The considered ontologies are classified according to their level of detail for both static facts and dynamic aspects. Furthermore, the ontologies are evaluated based on the presence of important entity classes and the relations between them.

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Skeleton model based behavior recognition for pedestrians and cyclists from vehicle sce ne camera

Cited by 8 publications

References 27 publications

Human Fall Down Recognition Using Coordinates Key Points Skeleton

Human Fall Down Recognition Using Coordinates Key Points Skeleton

Intention Recognition of Pedestrians and Cyclists by 2D Pose Estimation

From Traffic Sensor Data To Semantic Traffic Descriptions: The Test Area Autonomous Driving Baden-Württemberg Dataset (TAF-BW Dataset)

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