Pointing Direction Estimation for Attention Target Extraction Using Body-mounted Camera

Author(s):  
Yusei Oozono ◽  
Hirotake Yamazoe ◽  
Joo-Ho Lee
Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5228
Author(s):  
Jin-Cheol Kim ◽  
Hwi-Gu Jeong ◽  
Seongwook Lee

In this study, we propose a method to identify the type of target and simultaneously determine its moving direction in a millimeter-wave radar system. First, using a frequency-modulated continuous wave (FMCW) radar sensor with the center frequency of 62 GHz, radar sensor data for a pedestrian, a cyclist, and a car are obtained in the test field. Then, a You Only Look Once (YOLO)-based network is trained with the sensor data to perform simultaneous target classification and moving direction estimation. To generate input data suitable for the deep learning-based classifier, a method of converting the radar detection result into an image form is also proposed. With the proposed method, we can identify the type of each target and its direction of movement with an accuracy of over 95%. Moreover, the pre-trained classifier shows an identification accuracy of 85% even for newly acquired data that have not been used for training.


Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 251
Author(s):  
Yan Yan ◽  
Faguo Zhou ◽  
Yifan Ge ◽  
Cheng Liu ◽  
Jingwu Feng

With the spread of mobile applications and online interactive platforms, the number of user reviews are increasing explosively and becoming one of the most important ways for users to voice opinions. Opinion target extraction and opinion word extraction are two key procedures used to determine the helpfulness of reviews. In this paper, we implement a system to extract “opinion target:opinion word” pairs based on the Conditional Random Field (CRF). Firstly, we used the CRF model to extract opinion targets and opinion words, then combined these into pairs in order. In addition, Node.js was used to build a visualization system to display “opinion target:opinion word” pairs. In order to verify the effectiveness of the system, experiments were conducted on the Laptop and Restaurant datasets of SemEval-2014-task4, and the accuracy of the F value extracted by the model reached 86% and 90%, respectively. All the code and datasets for this experiment are available on GitHub.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Georges Hattab ◽  
Adamantini Hatzipanayioti ◽  
Anna Klimova ◽  
Micha Pfeiffer ◽  
Peter Klausing ◽  
...  

AbstractRecent technological advances have made Virtual Reality (VR) attractive in both research and real world applications such as training, rehabilitation, and gaming. Although these other fields benefited from VR technology, it remains unclear whether VR contributes to better spatial understanding and training in the context of surgical planning. In this study, we evaluated the use of VR by comparing the recall of spatial information in two learning conditions: a head-mounted display (HMD) and a desktop screen (DT). Specifically, we explored (a) a scene understanding and then (b) a direction estimation task using two 3D models (i.e., a liver and a pyramid). In the scene understanding task, participants had to navigate the rendered the 3D models by means of rotation, zoom and transparency in order to substantially identify the spatial relationships among its internal objects. In the subsequent direction estimation task, participants had to point at a previously identified target object, i.e., internal sphere, on a materialized 3D-printed version of the model using a tracked pointing tool. Results showed that the learning condition (HMD or DT) did not influence participants’ memory and confidence ratings of the models. In contrast, the model type, that is, whether the model to be recalled was a liver or a pyramid significantly affected participants’ memory about the internal structure of the model. Furthermore, localizing the internal position of the target sphere was also unaffected by participants’ previous experience of the model via HMD or DT. Overall, results provide novel insights on the use of VR in a surgical planning scenario and have paramount implications in medical learning by shedding light on the mental model we make to recall spatial structures.


2011 ◽  
Vol 179-180 ◽  
pp. 1342-1345
Author(s):  
Ping Chuan Zhang ◽  
Li Min Hou ◽  
Bu Yin Li

Passive radar based on GSM is a hot research field of new illuminators passive radars, and the wave arrival direction estimation is the key problem for detecting target. This paper designed adaptive antenna array for the GSM passive radar system, and give the complete Matlab simulation to verify the execution of the schedule, meanwhile, the result shows that the MUSIC algorithms is high accurate in the wave arrival direction compared with the Capon. All of this made a useful contribution to the research and application of the GSM-based passive radar.


2014 ◽  
Vol 989-994 ◽  
pp. 4107-4110
Author(s):  
Shao Jun Guo ◽  
Zhe Wang

The space-based visible observation imaging platform for sky targets is influenced by many factors, a serious factor is the light of background too bright. A image with the bright stray light background has some high gray areas those may submerge the targets info. Aimed at the shortcomings of traditional background removal method in target extraction under the bright stray light background, according to the differences of bright stray light background and sky targets imaging characteristics, this paper has made some research of the algorithms about how to remove the bright stray light background but not delete the targets info. The algorithm we got that give us great results will be shown in the paper. It solves the problems of the bright background light removal and greatly retain the targets info which submerged in the bright areas.


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