人工智能技术学报(英文)Pub Date : 2020-12-28DOI: 10.37965/jait.2020.0040
Louise Cronjé, I. Sanders
{"title":"Semiautomated Class Attendance Monitoring Using Smartphone Technology","authors":"Louise Cronjé, I. Sanders","doi":"10.37965/jait.2020.0040","DOIUrl":"https://doi.org/10.37965/jait.2020.0040","url":null,"abstract":"Class attendance is important. Class attendance recording is often done using ‘roll-call’ or signing attendance registers. These are time consuming, easy to cheat and it is difficult to draw any information from them. There are other, expensive alternatives to automate attendance recording with varying accuracy. This study experimented with a smart phone camera and different combinations of face detection and recognition algorithms to determine if it can be used to record attendance successfully, while keeping the solution cost-effective. The effect of different class sizes was also investigated. The research was done within a pragmatism philosophy, using a prototype in a field experiment. The algorithms that were used, are: Viola-Jones (HAAR features), Deep Neural Network (DNN) and Histogram of Oriented Gradients (HOG) for detection and Eigenfaces, Fisherfaces and Local Binary Pattern Histogram (LBPH) for recognition. The best combination was Viola-Jones combined with Fisherfaces, with a mean accuracy of 54% for a class of 10 students and 34.5% for a class of 22 students. The best all over performance on a single class photo was 70% (class size 10). As is, this prototype is not accurate enough to use, but with a few adjustments, it may become a cheap, easy-to-implement solution to the attendance recording problem.","PeriodicalId":70996,"journal":{"name":"人工智能技术学报(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45595417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
人工智能技术学报(英文)Pub Date : 2020-12-28DOI: 10.37965/jait.2020.0025
Hao Chen, Hong Zheng, Xiaolong Li
{"title":"Detection of Multiscale Center Point Objects Based on Parallel Network","authors":"Hao Chen, Hong Zheng, Xiaolong Li","doi":"10.37965/jait.2020.0025","DOIUrl":"https://doi.org/10.37965/jait.2020.0025","url":null,"abstract":"Anchor-based detectors are widely used in object detection. To improve the accuracy of object detection, multiple anchor boxes are intensively placed on the input image, yet. Most of which are invalid. Although the anchor-free method can reduce the number of useless anchor boxes, the invalid ones still occupy a high proportion. On this basis, this paper proposes a multi-scale center point object detection method based on parallel network to further reduce the number of useless anchor boxes. This study adopts the parallel network architecture of hourglass-104 and darknet-53 of which the first one outputs heatmaps to generate the center point for object feature location on the output attribute feature map of darknet-53. Combining feature pyramid and CIOU loss function this algorithm is trained and tested on MSCOCO dataset, increasing the detection rate of target location and the accuracy rate of small object detection. Though resembling the state-of-the-art two-stage detectors in overall object detection accuracy speed.","PeriodicalId":70996,"journal":{"name":"人工智能技术学报(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45227334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
求助内容：
应助结果提醒方式：