Modeling Human Activities via Long Short Term Memory Networks

Solmaz, Berkan
Karaman, Kaan
The presence of rapidly increasing visual data adds importance to the computer vision studies for automatic analysis and interpretation of content. Although the nervous and sensory systems in humans easily perform the processes such as understanding and recognizing activities that take place on a stage, these processes are among the most challenging research topics of computer vision. The activities vary according to the number of participants. For instance, a single person can perform activities consisting of various atomic actions. In the scenes with more than one person, interactions occur between people. Since interactions are mutual movements between multiple people, both temporal changes in the scene and the spatial structures need to be modeled for analysis. In this study, long short term memory networks and support vector machines, based on the positions and distances of human body joints, are trained for the automated classification of actions and interactions.
27th Signal Processing and Communications Applications Conference (SIU)


Identifying textual personal information using bidirectional LSTM networks
Ertekin Bolelli, Şeyda (2018-07-09)
Data-driven approaches based on the data collected from individuals are improving everyday life as a result of the developments in big data studies. Prior to developing such an approach, removal of personal information from the data is important since personal information contained in data would jeopardize people's privacy and may harm related individuals. Especially in the field of health sciences, identifying personal information in the collected data is a difficult task as most of the data collected in h...
Learning to rank web data using multivariate adaptive regression splines
Altınok, Gülşah; Batmaz, İnci; Karagöz, Pınar; Department of Statistics (2018)
A new trend, called learning to rank, has recently come to light in a wide variety of applications in Information Retrieval (IR), Natural Language Processing (NLP), and Data Mining (DM), to utilize machine learning techniques to automatically build the ranking models. Typical applications are document retrieval, expert search, definition search, collaborative filtering, question answering, and machine translation. In IR, there are three approaches used for ranking. The one is traditional model approaches su...
Modeling, inference and optimization of regulatory networks based on time series data
Weber, Gerhard Wilhelm; DEFTERLİ, ÖZLEM; ALPARSLAN GÖK, Sırma Zeynep; Kropat, Erik (2011-05-16)
In this survey paper, we present advances achieved during the last years in the development and use of OR, in particular, optimization methods in the new gene-environment and eco-finance networks, based on usually finite data series, with an emphasis on uncertainty in them and in the interactions of the model items. Indeed, our networks represent models in the form of time-continuous and time-discrete dynamics, whose unknown parameters we estimate under constraints on complexity and regularization by variou...
A case study in weather pattern searching using a spatial data warehouse model
Köylü, Çağlar; Akyürek, Sevda Zuhal; Department of Geodetic and Geographical Information Technologies (2008)
Data warehousing and Online Analytical Processing (OLAP) technology has been used to access, visualize and analyze multidimensional, aggregated, and summarized data. Large part of data contains spatial components. Thus, these spatial components convey valuable information and must be included in exploration and analysis phases of a spatial decision support system (SDSS). On the other hand, Geographic Information Systems (GISs) provide a wide range of tools to analyze spatial phenomena and therefore must be ...
Using data analytics for collaboration patterns in distributed software team simulations
Dafoulas, Georgios A.; Serce, Fatma C.; SWİGGER, Kathleen; BRAZİLE, Robert; Alpaslan, Ferda Nur; Alpaslan, Ferda Nur; Milewski, Allen (2016-08-05)
This paper discusses how previous work on global software development learning teams is extended with the introduction of data analytics. The work is based on several years of studying student teams working in distributed software team simulations. The scope of this paper is twofold. First it demonstrates how data analytics can be used for the analysis of collaboration between members of distributed software teams. Second it describes the development of a dashboard to be used for the visualization of variou...
Citation Formats
B. Solmaz and K. Karaman, “Modeling Human Activities via Long Short Term Memory Networks,” Sivas Cumhuriyet Univ, Sivas, TURKEY, 2019, p. 0, Accessed: 00, 2020. [Online]. Available: