깊이 센서를 이용한 능동형태모델 기반의 객체 추적 방법
Active Shape Model-based Object Tracking using Depth Sensor
1
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
This study proposes technology using Active Shape Model to track the object separating it by depth-sensors. Unlike the common visual camera, the depth-sensor is not affected by the intensity of illumination, and therefore a more robust object can be extracted. The proposed algorithm removes the horizontal component from the information of the initial depth map and separates the object using the vertical component. In addition, it is also a more efficient morphology, and labeling to perform image correction and object extraction. By applying Active Shape Model to the information of an extracted object, it can track the object more robustly. Active Shape Model has a robust feature-to-object occlusion phenomenon. In comparison to visual camera-based object tracking algorithms, the proposed technology, using the existing depth of the sensor, is more efficient and robust at object tracking. Experimental results, show that the proposed ASM-based algorithm using depth sensor can robustly track objects in real-time.
Abstract
This study proposes technology using Active Shape Model to track the object separating it by depth-sensors. Unlike the common visual camera, the depth-sensor is not affected by the intensity of illumination, and therefore a more robust object can be extracted. The proposed algorithm removes the horizontal component from the information of the initial depth map and separates the object using the vertical component. In addition, it is also a more efficient morphology, and labeling to perform image correction and object extraction. By applying Active Shape Model to the information of an extracted object, it can track the object more robustly. Active Shape Model has a robust feature-to-object occlusion phenomenon. In comparison to visual camera-based object tracking algorithms, the proposed technology, using the existing depth of the sensor, is more efficient and robust at object tracking. Experimental results, show that the proposed ASM-based algorithm using depth sensor can robustly track objects in real-time.
참고문헌 (16)
- 이경미, "조명변화와 겹침에 강건한 적응적 모델 기반 다중객체 추적", 정보과학회논문지 : 소프트웨어 및 응용, 32(5), pp.449~460 (2005), 한국정보과학회
- 김영로, "히스토그램 스트레칭을 이용한 효율적인 명암 향상 알고리즘", (사)디지털산업정보학회 논문지, 6(2), pp.193~198 (2010), (사)디지털산업정보학회
- D. Olmeda, "Detection and Tracking of Pedestrians in Infrared Images", International Conference on Signals, Circuits and Systems, pp.1~6 (2009)
- J. Kang, "Detection and Tracking of Moving Objects from Overlapping EO and IR Sensors", Computer Vision and Pattern Recognition Workshop, 47, pp.123~128 (2004)
- L. Mihaylova, "The Influence of Multi-Sensor Video Fusion on Object Tracking Using a Particle Filter", Informatik für Menschen. Gesellschaft für Informatik, Bonn, 47, pp.354~358 (2008)
- 고정환, "Estimation of Person Height and 3D Location using Stereo Tracking System", (사)디지털산업정보학회 논문지, 8(2), pp.95~104 (2012), (사)디지털산업정보학회[DOI]
- 김대희, "능동윤곽모델과 움직임 추정을 결합한 실시간 객체 추적 기술", 전자공학회논문지 - SP, 44(5), pp.64~72 (2007), 대한전자공학회
- 성만규, "깊이맵 센서를 이용한 3D캐릭터 가상공간 내비게이션 동작 합성 및 제어 방법", 멀티미디어학회논문지, 15(6), pp.827~836 (2012), 한국멀티미디어학회[DOI]
- T. Aach, "Statistical Model-based Change Detection in Moving Video", Signal Processing, 31, pp.165~180 (1993)
- J. Choi, "Spatio-Temporal Video Segmentation using a Joint Similarity Measure", IEEE trans. Circuit and System for Video Technology, 7, pp.297~286 (1997)
- J. Choi, "A User-Assisted Segmentation Method for Video Object Plane Generation", ITC-CSCC, pp.7~10 (1998)
- M. Hotter, "Image Segmentation Based on Object Oriented Mapping Parameter Estimation", Signal Processing, 15, pp.315~334 (1998)
- L. Vincent, "Watersheds in Digital Space : An Efficient Algorithm Based on Immersion Simulations", IEEE Trans. Pattern Analysis and Machine Intelligence, 13(6), pp.1022~1034 (1991)
- D. Park, "Fast Object Tracking in Digital Video", IEEE Trans. Consumer Electronics, 46(3), pp.785~790 (2000)
- M. Kass, "Snakes : Active Contour Models", International Journal of Computor Vision, 1, pp.321~332 (1998)
- T. Cootes, "Active Shape Models", Proc. 3rd British Machine Vision Conference, 3, pp.266~275 (1992)