Real Time Moving Object Detection Based on Frame Difference and Doppler Effects in HSV color model

HSV 컬러 모델에서의 도플러 효과와 영상 차분 기반의 실시간 움직임 물체 검출

  • 누완 (공주대학교 전기전자제어공학부) ;
  • 김원호 (공주대학교 전기전자제어공학부)
  • Received : 2014.10.27
  • Accepted : 2014.11.24
  • Published : 2014.12.31

Abstract

This paper propose a method to detect moving object and locating in real time from video sequence. first the proposed method extract moving object by differencing two consecutive frames from the video sequence. If the interval between captured two frames is long, it cause to generate fake moving object as tail of the real moving object. secondly this paper proposed method to overcome this problem by using doppler effects and HSV color model. finally the object segmentation and locating is done by combining the result that obtained from steps above. The proposed method has 99.2% of detection rate in practical and also this method is comparatively speed than other similar methods those proposed in past. Since the complexity of the algorithm is directly affects to the speed of the system, the proposed method can be used as low complexity algorithm for real time moving object detection.

본 논문은 영상에서 실시간으로 움직임 물체와 물체의 위치를 검출하는 방법을 제안한다. 첫째로 영상으로부터 2개의 연속된 프레임 차분을 통해 움직이는 물체를 추출하는 방법을 제안한다. 만약 두 프레임이 캡쳐되는 사이의 간격이 길다면, 실제 움직이는 물체의 꼬리 같은 거짓 움직임 물체를 생성한다. 두번째로 본 논문은 도플러 효과와 HSV 색상 모델을 사용하여 이 문제들을 해결하는 방법을 제안한다. 마지막으로 물체의 분할과 위치 설정은 상기의 단계에서 얻은 결과가 조합되어 완료된다. 제안된 방법은 99.2%의 검출율을 갖고, 과거에 제안된 다른 비슷한 방법들 보다는 비교적 빠른 속도를 갖는다. 알고리즘의 복잡성은 시스템의 속도에 직접적인 영향을 끼치기 때문에, 제안된 방법은 낮은 복잡성을 가져 실시간 움직임 검출을 위해 사용 될 수 있다.

Keywords

References

  1. Cucchiara, R. ; Grana, C. ; Piccardi, M. ; Prati, A, "Detecting moving objects, ghosts, and shadows in video streams", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25, pp. 1337-1342, 2003. https://doi.org/10.1109/TPAMI.2003.1233909
  2. Heikkila, M. ; Pietikainen, M, "A texture-based method for modeling the background and detecting moving objects", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28, pp. 657-662.
  3. Yongquan Xia ; Weili Li ; Shaohui Ning, "Moving Object Detection Algorithm Based on Variance Analysis", Second International Workshop on Computer Science and Engineering, Vol. 1, pp. 347-350, 2009.
  4. Zhan Chaohui ; Duan Xiaohui ; Xu Shuoyu ; Song Zheng, "An Improved Moving Object Detection Algorithm Based on Frame Difference and Edge Detection", Fourth International Conference on Image and Graphics, pp. 519-523, 2007.
  5. Diansheng Chen ; Yuxin Chen ; Tianmiao Wang, "Moving object detection by multi-view geometric constraints and flow vector classification", IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 1630-1634, 2010.
  6. Ping Gao ; Xiangju Sun ; Wei Wang, "Moving object detection based on kirsch operator combined with Optical Flow", International Conference on Image Analysis and Signal Processing (IASP), pp. 620-624, 2010.
  7. Klaiber, M. ; Rockstroh, L. ; Zhe Wang ; Baroud, Y, "A memory-efficient parallel single pass architecture for connected component labeling of streamed images", International Conference on Field-Programmable Technology (FPT), pp. 159-165 2012.
  8. Jabid, T. ; Mohammad, T. ; Ahsan, T. ; Abdullah-Al-Wadud, M, "An edge-texture based moving object detection for video content based application", 14th International Conference on Computer and Information Technology (ICCIT), pp. 112-116, 2011.
  9. Bo-Hao Chen ; Shih-Chia Huang, "An Advanced Moving Object Detection Algorithm for Automatic Traffic Monitoring in Real-World Limited Bandwidth Networks", IEEE Transactions on Multimedia, Vol. 16, pp. 837-847, 2014. https://doi.org/10.1109/TMM.2014.2298377
  10. Jiman Kim ; Guensu Ye ; Daijin Kim, "Moving object detection under free-moving camera", 17th IEEE International Conference on Image Processing (ICIP), pp. 4669-4672, 2010.