Gesture recognition for human-robot collaboration: A review

Liu, Hongyi; Wang, Lihui

doi:10.1016/j.ergon.2017.02.004

Cited by 337 publications

(154 citation statements)

References 27 publications

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“…Berri et al [20] demonstrated the capability of human face tracking as well as gesture recognition using a web camera and a depth camera from Microsoft Kinect, respectively. The depth camera in Kinect uses an infrared projector for active depth sensing [132].…”

Section: Contact-less Sensingmentioning

confidence: 99%

Symbiotic human-robot collaborative assembly

et al. 2019

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In human-robot collaborative assembly, robots are often required to dynamically change their pre-planned tasks to collaborate with human operators in a shared workspace. However, the robots used today are controlled by pre-generated rigid codes that cannot support effective human-robot collaboration. In response to this need, multi-modal yet symbiotic communication and control methods have been a focus in recent years. These methods include voice processing, gesture recognition, haptic interaction, and brainwave perception. Deep learning is used for classification, recognition and context awareness identification. Within this context, this keynote provides an overview of symbiotic human-robot collaborative assembly and highlights future research directions.

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Section: Contact-less Sensingmentioning

confidence: 99%

Symbiotic human-robot collaborative assembly

et al. 2019

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“…In this work, the implementation of the particle filter is intended to allow the integration of multiple sensing cues such as vehicle pose, heading, velocity, acceleration and turn signal information. In the future, this flexible design will be able to utilize vehicle to vehicle communication (V2V) [15] and driver gesture recognition [23] .…”

Section: Particle Filtermentioning

confidence: 99%

Estimating the Probabilities of Surrounding Vehicles’ Intentions and Trajectories using a Behavior Planner

Darweesh

Takeuchi

Takeda

2019

IJAE

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Estimating the intentions and trajectories of other vehicles is critical to achieving stable, long-term planning and decision making in autonomous driving systems. This paper introduces a novel technique for estimating the intention and trajectory probabilities of surrounding vehicles. The first step is to use a deterministic behavior planner to identify possible trajectories and behaviors. The behavior planner models an average driver who follows the driving rules according to information provided by a road network map, and also provides the control signal used in the following step. Next, a customized particle filter is integrated with the planner to model the uncertainty of various trajectories and behaviors, using multiple sensing cues such as pose, velocity, acceleration and turn signal use. The proposed method supports various sensor modalities, depending on the availability of additional sensing information. Finally, by including the sensor data the probabilistic process is able to estimate the probabilities of various trajectories and intentions. The proposed method is generic to any driving situation supported by the behavior planner. Intentions such as 'go forward', 'turn right', 'turn left', 'yield' and 'stop' are supported by the proposed method. Our proposed method is evaluated using multiple, complex, simulated driving situations, and then comparing the simulation's ground truth to the estimated probabilities. Evaluation criteria are how early and accurately our system can estimate the driving trajectory and intention probabilities of the vehicle. Our results show that the method can successfully estimate driver intention and trajectory in multiple complex situations, such as left turns, right turns and during passing, as well as at four-way intersections and bus stops.

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“…Modern approaches are mostly based on deep learning. Convolutional Neural Networks (CNNs) could succeed in giving the best results on image recognition, image segmentation and so forth [14][15][16]. Although one problem in the case of video domain is that these networks are designed for two-dimensional input images.…”

Section: Related Workmentioning

confidence: 99%

A Unified Method for First and Third Person Action Recognition

Javidani

Mahmoudi-Aznaveh

2018

Electrical Engineering (ICEE), Iranian Conference On

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In this paper, a new video classification methodology is proposed which can be applied in both first and third person videos. The main idea behind the proposed strategy is to capture complementary information of appearance and motion efficiently by performing two independent streams on the videos. The first stream is aimed to capture long-term motions from shorter ones by keeping track of how elements in optical flow images have changed over time. Optical flow images are described by pre-trained networks that have been trained on large scale image datasets. A set of multi-channel time series are obtained by aligning descriptions beside each other. For extracting motion features from these time series, PoT representation method plus a novel pooling operator is followed due to several advantages. The second stream is accomplished to extract appearance features which are vital in the case of video classification. The proposed method has been evaluated on both first and third-person datasets and results present that the proposed methodology reaches the state of the art successfully.

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Gesture recognition for human-robot collaboration: A review

Cited by 337 publications

References 27 publications

Symbiotic human-robot collaborative assembly

Symbiotic human-robot collaborative assembly

Estimating the Probabilities of Surrounding Vehicles’ Intentions and Trajectories using a Behavior Planner

A Unified Method for First and Third Person Action Recognition

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