A CRNN module for hand pose estimation

Hu, Zhongxu; Hu, Youmin; Liu, Jie; Han, Dongmin

doi:10.1016/j.neucom.2018.12.065

Cited by 24 publications

(14 citation statements)

References 12 publications

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“…Finally, the parallel layers must converge in a final layer that will merge all the gathered information. This method's improvement was presented by Hu et al [33], doing the same procedure of starting with a convolutional layer to extract image features and then feeding the data into the recurrent layers. This method proposes the implementation of the CRNN module in between any of the convolutional layers.…”

Section: Crnnmentioning

confidence: 99%

Object Detection, Distributed Cloud Computing and Parallelization Techniques for Autonomous Driving Systems

et al. 2021

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Autonomous vehicles are increasingly becoming a necessary trend towards building the smart cities of the future. Numerous proposals have been presented in recent years to tackle particular aspects of the working pipeline towards creating a functional end-to-end system, such as object detection, tracking, path planning, sentiment or intent detection, amongst others. Nevertheless, few efforts have been made to systematically compile all of these systems into a single proposal that also considers the real challenges these systems will have on the road, such as real-time computation, hardware capabilities, etc. This paper reviews the latest techniques towards creating our own end-to-end autonomous vehicle system, considering the state-of-the-art methods on object detection, and the possible incorporation of distributed systems and parallelization to deploy these methods. Our findings show that while techniques such as convolutional neural networks, recurrent neural networks, and long short-term memory can effectively handle the initial detection and path planning tasks, more efforts are required to implement cloud computing to reduce the computational time that these methods demand. Additionally, we have mapped different strategies to handle the parallelization task, both within and between the networks.

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Section: Crnnmentioning

confidence: 99%

Object Detection, Distributed Cloud Computing and Parallelization Techniques for Autonomous Driving Systems

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Finally, the parallel layers must converge in a final layer that will merge all the gathered information. This method's improvement was presented by Hu et al [34], doing the same procedure of starting with a convolutional layer to extract image features and then feeding the data into the recurrent layers. This method proposes the implementation of the CRNN module in between any of the convolutional layers.…”

Section: Crnnmentioning

confidence: 99%

Object Detection, Distributed Cloud Computing and Parallelization Techniques for Autonomous Driving Systems

Medina¹,

Espitia²,

Silva³

et al. 2021

Preprint

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Autonomous driving systems are increasingly becoming a necessary trend towards building smart cities of the future. Numerous proposals have been presented in recent years to tackle particular aspects of the working pipeline towards creating a functional end-to-end system, such as object detection, tracking, path planning, sentiment or intent detection. Nevertheless, few efforts have been made to systematically compile all of these systems into a single proposal that effectively considers the real challenges these systems will have on the road, such as real-time computation, hardware capabilities, etc. This paper has reviewed various techniques towards proposing our own end-to-end autonomous vehicle system, considering the latest state on the art on computer vision, DSs, path planning, and parallelization.

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“…However, this method could appear to be biased in assigning interaction labels. In the research process, Hu et al, respectively, proposed an improved Bernoulli heatmap [26] and a new convolutional recurrent network model [27] to estimate the joint point information of various parts of the human body. Although these methods can quickly and accurately construct a human head joint point model, the performance needs to be improved when processing large-angle samples.…”

Section: Key Point Predictionmentioning

confidence: 99%

At the Traffic Intersection, Stopping, or Walking? Pedestrian Path Prediction Based on KPOF-GPDM for Driving Assistance

Long¹,

Zhang

Zhao³

et al. 2021

Journal of Advanced Transportation

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Pedestrian detection has always been a research hotspot in the Advanced Driving Assistance System (ADAS) with great progress in recent years. However, for the ADAS, we not only need to detect the behavior of pedestrians in front of the vehicle but also predict future action and the motion trajectory. Therefore, in this paper, we propose a human key point combined optical flow network (KPOF-Net) in the vehicle ADAS for the occlusion situation in the actual scene. When the vehicle encounters a blocked pedestrian at a traffic intersection, we used self-flow to estimate the global optical flow in the image sequence and then proposed a White Edge Cutting (WEC) algorithm to remove obstructions and simply modified the generative adversarial network to initialize pedestrians behind the obstructions. Next, we extracted pedestrian optical flow information and human joint point information in parallel, among which we trained four human key point models suitable for traffic intersections. At last, KPOF-GPDM fusion was proposed to predict the future status and walking trajectories of pedestrians, which combined optical flow information with human key point information. In the experiment, we did not merely compare our method with other four representative approaches in the same scene sequences. We also verified the accuracy of the pedestrian motion state and motion trajectory prediction of the system after fusion of human joint points and optical flow information. Taking into account the real-time performance of the system, in the low-speed and barrier-free environment, the comparative analysis only uses optical flow information, human joint point information, and KPOF-Net three prediction models. The results show that (1) in the same traffic environment, our proposed KPOF-Net can predict the change of pedestrian motion state about 5 frames (about 0.26 s) ahead of other excellent systems; (2) at the same time, our system predicts the trajectory of the pedestrian more accurately than the other four systems, which can achieve more stable minimum error ±0.04 m; (3) in a low-speed, barrier-free experimental environment, our proposed trajectory prediction model that integrates human joint points and optical flow information has higher prediction accuracy and smaller fluctuations than a single-information prediction model, and it can be well applied to automobiles’ ADAS.

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A CRNN module for hand pose estimation

Cited by 24 publications

References 12 publications

Object Detection, Distributed Cloud Computing and Parallelization Techniques for Autonomous Driving Systems

Object Detection, Distributed Cloud Computing and Parallelization Techniques for Autonomous Driving Systems

Object Detection, Distributed Cloud Computing and Parallelization Techniques for Autonomous Driving Systems

At the Traffic Intersection, Stopping, or Walking? Pedestrian Path Prediction Based on KPOF-GPDM for Driving Assistance

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