Browsing by Author "Chen, Lun-Chi"

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IDS-DLA: Sheet Metal Part Identification System for Process Automation Using Deep Learning Algorithms
(2020-01-01)
Sheu, Ruey-Kai
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Lin, Yuan-Cheng
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Huang, Chin-Yin
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Chen, Lun-Chi
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Pardeshi, Mayuresh Sunil
Sheet metal-based manufacturing industries operate on several varieties of sheet metal parts. Previously QR codes stickers were put on sheet metal parts for identification by manual workers as per their respective shape and size features, thus ensuring synchronized raw material flow for the manufacturing process. However, identifying a particular type of sheet metal part based on its different features is still a challenge for a trained manual operator. Currently, in the market, there exist some automation solutions for solving such kind of problem but are incapable of achieving better performance and possess accuracy issues. So our goal is to provide process automation by overcoming manual work-based dependency and limitations. Therefore, a system is required that can take input a high definition camera captured sheet metal part image and provide an accurately identified type as output by utilizing a deep learning classification model in computer vision. The automation of sheet metal part identification by using ERP, CAD files and scheduling among them would make a smooth workflow by IDS-DLA. This paper aims to solve the identification problem by using the design and implementation of a sheet metal part identification, given as sheet metal part IDentification System for process automation using Deep Learning Algorithms (IDS-DLA). Considering the sheet metal parts there exists a large volume of types but fewer quantities. IDS-DLA performs high accuracy sheet metal part identification from the CAD model database by using the Geometric and CNN triplet filter. The IDS-DLA also evaluates the Hu moment ranking to choose the top 5 rank predictions as final ranking results. The applications can be given as manufacturing process automation for industry, 2D CAD search, 2D measuring solution, closet formation, etc. Ultimately, from the experiments, it can be observed that better accuracy is obtained as compared with the previous benchmarks. This multi-filtering approach using a deep feature extraction algorithm concludes to be the better approach and achieves higher performance.
STAM-CCF: Suspicious Tracking Across Multiple Camera Based on Correlation Filters
(2019-07-01)
Sheu, Ruey-Kai
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Pardeshi, Mayuresh
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Chen, Lun-Chi
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Yuan, Shyan-Ming
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交大名義發表
There is strong demand for real-time suspicious tracking across multiple cameras in intelligent video surveillance for public areas, such as universities, airports and factories. Most criminal events show that the nature of suspicious behavior are carried out by un-known people who try to hide themselves as much as possible. Previous learning-based studies collected a large volume data set to train a learning model to detect humans across multiple cameras but failed to recognize newcomers. There are also several feature-based studies aimed to identify humans within-camera tracking. It would be very difficult for those methods to get necessary feature information in multi-camera scenarios and scenes. It is the purpose of this study to design and implement a suspicious tracking mechanism across multiple cameras based on correlation filters, called suspicious tracking across multiple cameras based on correlation filters (STAM-CCF). By leveraging the geographical information of cameras and YOLO object detection framework, STAM-CCF adjusts human identification and prevents errors caused by information loss in case of object occlusion and overlapping for within-camera tracking cases. STAM-CCF also introduces a camera correlation model and a two-stage gait recognition strategy to deal with problems of re-identification across multiple cameras. Experimental results show that the proposed method performs well with highly acceptable accuracy. The evidences also show that the proposed STAM-CCF method can continuously recognize suspicious behavior within-camera tracking and re-identify it successfully across multiple cameras.