Visual Path Prediction in Complex Scenes with Crowded Moving Objects

Yoo, Youngjoon; Yun, Kimin; Yun, Sangdoo; Hong, Jonghee; Jeong, Hawook; Choi, Jin Young

doi:10.1109/cvpr.2016.292

Cited by 26 publications

(27 citation statements)

References 38 publications

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“…According to the above, as Figure 5, we have formalized the semantic understanding of images into object detection and position matching. By contrast with those mentioned in papers [7,8] earlier, the image reward modeling in our paper is not only related to the moving target, but also with the fixed target. In this paper, aiming at the uncertainty of the visual context model in the prediction of the path ending point, the end point position matching equation is established as the constraint term of the loss function according to the principle of pixel matching of the moving target and fixed target.…”

Section: Trajectory Prediction Networkmentioning

confidence: 66%

“…Contrast Experiment: Since there are no works on the motion trajectory prediction and modeling of peg-in-hole alignment of precast concrete members, we can only compare the method in this paper with the two existing prediction methods [7,8] in our data set. Since the existing prediction methods are different from the prediction tasks in this paper, the methods and parameters provided by them are used for experiments.…”

Section: Resultsmentioning

confidence: 99%

“…The key part of the system is the precision of 3d vision detection, which will be affected when gray-scale distortion occurs, matching problems of special structure covering a scene [5]. Obviously, due to the characteristics of few sensors and simple and convenient application, the intelligent system based on visual images has been applied to various fields [6][7][8]. Cireşan [9] combine various Deep Neural Networks (DNNs) trained Symmetry 2019, 11, 629 3 of 20 on differently preprocessed data into a multi-column DNN, use supervised learning to identify and classify traffic signs.…”

Section: Computer Visionmentioning

confidence: 99%

“…The CNNs have given the state-of-the-art performance on various two-dimensional image processing and prediction tasks [24][25][26][27][28]. At present, a deep learning framework for motion direction and trajectory prediction is mainly applied in panoramic video head motion prediction [7], and the prediction and planning of the motion path of moving objects [8]. Xu [7] found that deep reinforcement learning (DRL) can be applied to predict head movement (HM) positions, via maximizing the reward of imitating human HM scanpaths through the agent's actions.…”

Section: Modeling and Prediction Based On Deep Learningmentioning

confidence: 99%

“…Xu [7] found that deep reinforcement learning (DRL) can be applied to predict head movement (HM) positions, via maximizing the reward of imitating human HM scanpaths through the agent's actions. Yoo [8] considered moving dynamics of co-occurring objects for path prediction in a scene that includes crowded moving objects. He presented an algorithm to find the future location of any target object by utilizing the unsupervised learning results from the model.…”

Section: Modeling and Prediction Based On Deep Learningmentioning

confidence: 99%

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Trajectory Prediction of Assembly Alignment of Columnar Precast Concrete Members with Deep Learning

2019

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During the construction of prefabricated building, there are some problems such as a time consuming, low-level of automation when precast concrete members are assembled and positioned. This paper presents vision-based intelligent assembly alignment guiding technology for columnar precast concrete members. We study the video images of assembly alignment of the hole at the bottom of the precast concrete members and the rebar on the ground. Our goal is to predict the trajectory of the moving target in a future moment and the movement direction at each position during the alignment process by assembly image sequences. However, trajectory prediction is still subject to the following challenges: (1) the effect of external environment (illumination) on image quality; (2) small target detection in complex backgrounds; (3) low accuracy of trajectory prediction results based on the visual context model. In this paper, we use mask and adaptive histogram equalization to improve the quality of the image and improved method to detect the targets. In addition, aiming at the low position precision of trajectory prediction based on the context model, we propose the end point position-matching equation according to the principle of end point pixel matching of the moving target and fixed target, as the constraint term of the loss function to improve the prediction accuracy of the network. In order to evaluate comprehensively the performance of the proposed method on the trajectory prediction in the assembly alignment task, we construct the image dataset, use Hausdorff distance as the evaluation index, and compare with existing prediction methods. The experimental results show that, this framework is better than the existing methods in accuracy and robustness at the prediction of assembly alignment motion trajectory of columnar precast concrete members.

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Section: Trajectory Prediction Networkmentioning

confidence: 66%

Section: Resultsmentioning

confidence: 99%