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Detection Human Motion with Heel Strikes for Surveillance Analysis

Detection Human Motion with Heel Strikes for Surveillance Analysis
Detection Human Motion with Heel Strikes for Surveillance Analysis
Heel strike detection is an important cue for human gait recognition and detection in visual surveillance since the heel strike position can be used to derive the gait periodicity, stride and step length. We propose a novel method for heel strike detection using a gait trajectory model, which is robust to occlusion, camera view and to low resolution which can generalize to a variety of surveillance imagery. When a person walks, the movement of the head is conspicuous and sinusoidal. The highest point of the trajectory of the head occurs when the feet cross. Our gait trajectory model is constructed from trajectory data using non-linear optimization. Then, the key frames in which the heel strike takes place are extracted. A Region Of Interest (ROI) is extracted using the silhouette image of the key frame as a filter. Finally, gradient descent is applied to detect maxima which are considered to be the time of the heel strikes. The experimental results show a detection rate of 95% on two databases. The contribution of this research is the first use of the gait trajectory in the heel strike position estimation process and we contend that the approach is a new approach for basic analysis in surveillance imagery.
Heel strike detection, gait trajectory model, gradient descent, gait
978-3-642-23671-6
9-16
Jung, Sung Uk
7d4ceed9-1bc6-4740-a398-b81a670438ba
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Jung, Sung Uk
7d4ceed9-1bc6-4740-a398-b81a670438ba
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12

Jung, Sung Uk and Nixon, Mark (2011) Detection Human Motion with Heel Strikes for Surveillance Analysis. International Conference on Computer Analysis of Images and Patterns, Spain. 29 - 31 Aug 2011. pp. 9-16 .

Record type: Conference or Workshop Item (Paper)

Abstract

Heel strike detection is an important cue for human gait recognition and detection in visual surveillance since the heel strike position can be used to derive the gait periodicity, stride and step length. We propose a novel method for heel strike detection using a gait trajectory model, which is robust to occlusion, camera view and to low resolution which can generalize to a variety of surveillance imagery. When a person walks, the movement of the head is conspicuous and sinusoidal. The highest point of the trajectory of the head occurs when the feet cross. Our gait trajectory model is constructed from trajectory data using non-linear optimization. Then, the key frames in which the heel strike takes place are extracted. A Region Of Interest (ROI) is extracted using the silhouette image of the key frame as a filter. Finally, gradient descent is applied to detect maxima which are considered to be the time of the heel strikes. The experimental results show a detection rate of 95% on two databases. The contribution of this research is the first use of the gait trajectory in the heel strike position estimation process and we contend that the approach is a new approach for basic analysis in surveillance imagery.

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Published date: 29 August 2011
Additional Information: Event Dates: Aug. 29-31, 2011
Venue - Dates: International Conference on Computer Analysis of Images and Patterns, Spain, 2011-08-29 - 2011-08-31
Keywords: Heel strike detection, gait trajectory model, gradient descent, gait
Organisations: Vision, Learning and Control

Identifiers

Local EPrints ID: 273215
URI: http://eprints.soton.ac.uk/id/eprint/273215
ISBN: 978-3-642-23671-6
PURE UUID: 98f5a2a5-3e7c-41fd-bda3-652b56ac4c5c
ORCID for Mark Nixon: ORCID iD orcid.org/0000-0002-9174-5934

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Date deposited: 17 Feb 2012 10:58
Last modified: 03 Dec 2019 02:07

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Contributors

Author: Sung Uk Jung
Author: Mark Nixon ORCID iD

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