High Level Activity Recognition using Low Resolution Wearable Vision

Sudeep Sundaram; Walterio Mayol-Cuevas

High Level Activity Recognition using Low Resolution Wearable Vision

Sudeep Sundaram
, Walterio Mayol-Cuevas

Bristol Robotics Laboratory

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

39 Citations (Scopus)

Abstract

This paper presents a system aimed to serve as the enabling platform for a wearable assistant. The method observes manipulations from a wearable camera and classifies activities from roughly stabilized low resolution images (160x120 pixels) with the help of a 3-level Dynamic Bayesian Network and adapted temporal templates. Our motivation is to explore robust but computationally inexpensive visual methods to perform as much activity inference as possible without resorting to more complex object or hand detectors. The description of the method and results obtained are presented, as well as the motivation for further work in the area of wearable visual sensing.

Translated title of the contribution	High Level Activity Recognition using Low Resolution Wearable Vision
Original language	English
Title of host publication	First Workshop on Egocentric Vision, in conjunction with the International Conference on Computer Vision and Pattern Recognition
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Publication status	Published - 2009

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Conference Proceedings/Title of Journal: First Workshop on Egocentric Vision, in conjunction with the International Conference on Computer Vision and Pattern Recognition
Other identifier: 2001033

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title = "High Level Activity Recognition using Low Resolution Wearable Vision",

abstract = "This paper presents a system aimed to serve as the enabling platform for a wearable assistant. The method observes manipulations from a wearable camera and classifies activities from roughly stabilized low resolution images (160x120 pixels) with the help of a 3-level Dynamic Bayesian Network and adapted temporal templates. Our motivation is to explore robust but computationally inexpensive visual methods to perform as much activity inference as possible without resorting to more complex object or hand detectors. The description of the method and results obtained are presented, as well as the motivation for further work in the area of wearable visual sensing. ",

author = "Sudeep Sundaram and Walterio Mayol-Cuevas",

note = "Other page information: - Conference Proceedings/Title of Journal: First Workshop on Egocentric Vision, in conjunction with the International Conference on Computer Vision and Pattern Recognition Other identifier: 2001033",

year = "2009",

language = "English",

booktitle = "First Workshop on Egocentric Vision, in conjunction with the International Conference on Computer Vision and Pattern Recognition",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

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High Level Activity Recognition using Low Resolution Wearable Vision. / Sundaram, Sudeep; Mayol-Cuevas, Walterio.
First Workshop on Egocentric Vision, in conjunction with the International Conference on Computer Vision and Pattern Recognition. Institute of Electrical and Electronics Engineers (IEEE), 2009.

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

TY - GEN

T1 - High Level Activity Recognition using Low Resolution Wearable Vision

AU - Sundaram, Sudeep

AU - Mayol-Cuevas, Walterio

N1 - Other page information: - Conference Proceedings/Title of Journal: First Workshop on Egocentric Vision, in conjunction with the International Conference on Computer Vision and Pattern Recognition Other identifier: 2001033

PY - 2009

Y1 - 2009

N2 - This paper presents a system aimed to serve as the enabling platform for a wearable assistant. The method observes manipulations from a wearable camera and classifies activities from roughly stabilized low resolution images (160x120 pixels) with the help of a 3-level Dynamic Bayesian Network and adapted temporal templates. Our motivation is to explore robust but computationally inexpensive visual methods to perform as much activity inference as possible without resorting to more complex object or hand detectors. The description of the method and results obtained are presented, as well as the motivation for further work in the area of wearable visual sensing.

AB - This paper presents a system aimed to serve as the enabling platform for a wearable assistant. The method observes manipulations from a wearable camera and classifies activities from roughly stabilized low resolution images (160x120 pixels) with the help of a 3-level Dynamic Bayesian Network and adapted temporal templates. Our motivation is to explore robust but computationally inexpensive visual methods to perform as much activity inference as possible without resorting to more complex object or hand detectors. The description of the method and results obtained are presented, as well as the motivation for further work in the area of wearable visual sensing.

M3 - Conference Contribution (Conference Proceeding)

BT - First Workshop on Egocentric Vision, in conjunction with the International Conference on Computer Vision and Pattern Recognition

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -

High Level Activity Recognition using Low Resolution Wearable Vision

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