Recognizing actions from still images

Download

index.pdf

Date

2008-12-11

Author

İKİZLER CİNBİŞ, NAZLI
Cinbiş, Ramazan Gökberk
PEHLİVAN, SELEN
DUYGULU ŞAHİN, PINAR

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

114
views

42
downloads

In this paper, we approach the problem of understanding human actions from still images. Our method involves representing the pose with a spatial and orientational histogramming of rectangular regions on a parse probability map. We use LDA to obtain a more compact and discriminative feature representation and binary SVMs for classification. Our results over a new dataset collected for this problem show that by using a rectangle histogramming approach, we can discriminate actions to a great extent. We also show how we can use this approach in an unsupervised setting. To our best knowledge, this is one of the first studies that try to recognize actions within still images.

Subject Keywords

Image recognition, Image edge detection, Shape measurement, Linear discriminant analysis, Testing, Application software, Surveillance, Human computer interaction, Biological system modeling, Histograms

URI

https://hdl.handle.net/11511/34317

DOI

https://doi.org/10.1109/icpr.2008.4761663

Conference Name

19th International Conference on Pattern Recognition

Collections

Department of Computer Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Learning Context on a Humanoid Robot using Incremental Latent Dirichlet Allocation Çelikkanat, Hande; Orhan, Guner; Pugeault, Nicolas; Guerin, Frank; Şahin, Erol; Kalkan, Sinan (Institute of Electrical and Electronics Engineers (IEEE), 2016-03-01) In this paper, we formalize and model context in terms of a set of concepts grounded in the sensorimotor interactions of a robot. The concepts are modeled as a web using Markov Random Field (MRF), inspired from the concept web hypothesis for representing concepts in humans. On this concept web, we treat context as a latent variable of Latent Dirichlet Allocation (LDA), which is a widely-used method in computational linguistics for modeling topics in texts. We extend the standard LDA method in order to make ...
Learning affordances for categorizing objects and their properties Dag̃, Nilgün; Atil, Ilkay; Kalkan, Sinan; Şahin, Erol (2010-11-18) In this paper, we demonstrate that simple interactions with objects in the environment leads to a manifestation of the perceptual properties of objects. This is achieved by deriving a condensed representation of the effects of actions (called effect prototypes in the paper), and investigating the relevance between perceptual features extracted from the objects and the actions that can be applied to them. With this at hand, we show that the agent can categorize (i.e., partition) its raw sensory perceptual fe...
Human activity classification using spatio-temporal feature relations Akpınar, Kutalmış; Ulusoy, İlkay; Department of Electrical and Electronics Engineering (2012) This thesis compares the state of the art methods and proposes solutions for human activity classification from video data. Human activity classification is finding the meaning of human activities, which are captured by the video. Classification of human activity is needed in order to improve surveillance video analysis and summarization, video data mining and robot intelligence. This thesis focuses on the classification of low level human activities which are used as an important information source to dete...
Human behavior understanding using video analysis Gökçe, Celal Onur; Halıcı, Uğur; Department of Electrical and Electronics Engineering (2016) In this study we proposed a new hierarchical architecture for solution of human behavior understanding problem. A new dataset, namely football video game (FVG) dataset, is generated which involves activities more complex than any other dataset present in the literature. Football ball is detected using multilayer neural networks trained with gradient descent algorithm and enhanced using learning with queries method and it is tracked using growing window algorithm. After region of interest is extracted around...
Imitation of Basic Hand Preshapes by Fluid Based Method: Fluidic Formation Control Erkmen, Aydan Müşerref; Erkmen, İsmet (2009-11-05) In this paper, a new approach is developed handling the correspondence problem due to the difference in embodiment between imitator and demonstrator in imitation learning. In our work the imitator is a fluidic system of dynamics totally different than the demonstrator which is human hand gestures. In this work we demonstrate the fluidic formation control so as to generate basic hand preshaping features. Our fluidic formation control is based on Smoothed Particle Hydrodynamics (SPH), which is a particle base...

Citation Formats

N. İKİZLER CİNBİŞ, R. G. Cinbiş, S. PEHLİVAN, and P. DUYGULU ŞAHİN, “Recognizing actions from still images,” presented at the 19th International Conference on Pattern Recognition, Tampa, FL, USA, 2008, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/34317.