TR2014-069

A Novel Video Dataset for Change Detection Benchmarking


    •  Goyette, N.; Jodoin, P.-M.; Porikli, F.; Konrad, J.; Ishwar, P., "A Novel Video Dataset for Change Detection Benchmarking", IEEE Transactions on Image Processing, DOI: 10.1109/TIP.2014.2346013, ISSN: 1057-7149, Vol. 23, No. 11, pp. 4663-4679, August 2014.
      BibTeX Download PDF
      • @article{Goyette2014aug,
      • author = {Goyette, N. and Jodoin, P.-M. and Porikli, F. and Konrad, J. and Ishwar, P.},
      • title = {A Novel Video Dataset for Change Detection Benchmarking},
      • journal = {IEEE Transactions on Image Processing},
      • year = 2014,
      • volume = 23,
      • number = 11,
      • pages = {4663--4679},
      • month = aug,
      • organization = {IEEE Signal Processing Society},
      • publisher = {IEEE},
      • doi = {10.1109/TIP.2014.2346013},
      • issn = {1057-7149},
      • url = {http://www.merl.com/publications/TR2014-069}
      • }
  • Research Area:

    Computer Vision


Change detection is one of the most commonly encountered low-level tasks in computer vision and video processing. A plethora of algorithms have been developed to date, yet no widely accepted, realistic, large-scale video data set exists for benchmarking different methods. Presented here is a unique change detection video data set consisting of nearly 90000 frames in 31 video sequences representing six categories selected to cover a wide range of challenges in two modalities (color and thermal infrared). A distinguishing characteristic of this benchmark video data set is that each frame is meticulously annotated by hand for ground-truth foreground, background, and shadow area boundaries-an effort that goes much beyond a simple binary label denoting the presence of change. This enables objective and precise quantitative comparison and ranking of video-based change detection algorithms. This paper discusses various aspects of the new data set, quantitative performance metrics used, and comparative results for over two dozen change detection algorithms. It draws important conclusions on solved and remaining issues in change detection, and describes future challenges for the scientific community. The data set, evaluation tools, and algorithm rankings are available to the public on a website1 and will be updated with feedback from academia and industry in the future.