Human shape tracking for gait recognition using active contours with mean shift

Authors:
Kyung Su Kwon;Se Hyun Park;Eun Yi Kim;Hang Joon Kim
Affiliations:
Department of Computer Engineering, Kyungpook National Univ., Korea;School of Computer and Communication, Daegu Univ., Korea;Department of Internet and Multimedia Engineering, Konkuk Univ., Korea;Department of Computer Engineering, Kyungpook National Univ., Korea
Venue:
HCI'07 Proceedings of the 12th international conference on Human-computer interaction: intelligent multimodal interaction environments
Year:
2007

Citing 10
Cited 1

The background primal sketch: an approach for tracking moving objects

Machine Vision and Applications
Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects

IEEE Transactions on Pattern Analysis and Machine Intelligence
Geodesic active contours

ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Silhouette-Based Human Identification from Body Shape and Gait

FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Gait Analysis for Recognition and Classification

FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Texture-Based Approach for Text Detection in Images Using Support Vector Machines and Continuously Adaptive Mean Shift Algorithm

IEEE Transactions on Pattern Analysis and Machine Intelligence
The HumanID Gait Challenge Problem: Data Sets, Performance, and Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence
Human Contour Extraction Using Level Set

CIT '05 Proceedings of the The Fifth International Conference on Computer and Information Technology
Statistical motion model based on the change of feature relationships: human gait-based recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic gait recognition based on statistical shape analysis

IEEE Transactions on Image Processing

A survey of advances in biometric gait recognition

CCBR'11 Proceedings of the 6th Chinese conference on Biometric recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we present a human shape extraction and tracking for gait recognition using geodesic active contour models(GACMs) combined with mean-shift algorithm. The active contour models (ACMs) are very effective to deal with the non-rigid object because of its elastic property, but they have the limitation that their performance is mainly dependent on the initial curve. To overcome this problem, we combine the mean-shift algorithm with the traditional GACMs. The main idea is very simple. Before evolving using level-set method, the initial curve in each frame is re-localized near the human region and is resized enough to include the targe object. This mechanism allows for reducing the number of iterations and for handling the large object motion. Our system is composed of human region detection and human shape tracking. In the human region detection module, the silhouette of a walking person is extracted by background subtraction and morphologic operation. Then human shape are correctly obtained by the GACMs with mean-shift algorithm. To evaluate the effectiveness of the proposed method, it is applied the common gait data, then the results show that the proposed method is extracted and tracked efficiently accurate shape for gait recognition.