J. Kuklyte, Kevin McGuinness, R. Hebbalaguppe, C. Direkoğlu, Leonardo Gualano, N. O’Connor
{"title":"Identification of moving objects in poor quality surveillance data","authors":"J. Kuklyte, Kevin McGuinness, R. Hebbalaguppe, C. Direkoğlu, Leonardo Gualano, N. O’Connor","doi":"10.1109/WIAMIS.2013.6616165","DOIUrl":"https://doi.org/10.1109/WIAMIS.2013.6616165","url":null,"abstract":"In a world of pervasive visual surveillance and fast computing there is a growing interest in automated surveillance analytics. Object classification can support existing event detection techniques by identifying objects present allowing confident prioritization of the detected events. In this paper we propose an effective object classification algorithm to distinguish between four classes that are important for outdoor surveillance applications: people, vehicles, animals and `other'. A challenging dataset that has been obtained from an industry partner from real deployments of poor quality cameras is used to evaluate the proposed approach. Frame differencing was found to be the most suitable approach to detect moving objects with Histogram of Oriented Gradients (HOG) the preferred choice to represent the objects. An SVM was used for classification. The results show that the proposed approach gives higher accuracy than a similar approach based on SIFT and bag words.","PeriodicalId":408077,"journal":{"name":"2013 14th International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121858227","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}
{"title":"Likability of human voices: A feature analysis and a neural network regression approach to automatic likability estimation","authors":"F. Eyben, F. Weninger, E. Marchi, Björn Schuller","doi":"10.1109/WIAMIS.2013.6616159","DOIUrl":"https://doi.org/10.1109/WIAMIS.2013.6616159","url":null,"abstract":"Recently, the automatic analysis of likability of a voice has become popular. This work follows up on our original work in this field and provides an in-depth discussion of the matter and an analysis of the acoustic parameters. We investigate the automatic analysis of voice likability in a continuous label space with neural networks as regressors and discuss the relevance of acoustic features. We provide results on the Speaker Likability Database for comparison with previous work and a subset of the TIMIT database for validation.","PeriodicalId":408077,"journal":{"name":"2013 14th International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128606690","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}