C4: A real-time object detection framework

Jianxin Wu, Nini Liu, Christopher Geyer, James M. Rehg

Research output: Contribution to journalArticlepeer-review


A real-time and accurate object detection framework, C4, is proposed in this paper. C4 achieves 20 fps speed and the state-of-the-art detection accuracy, using only one processing thread without resorting to special hardware such as GPU. The real-time accurate object detection is made possible by two contributions. First, we conjecture (with supporting experiments) that contour is what we should capture and signs of comparisons among neighboring pixels are the key information to capture contour cues. Second, we show that the CENTRIST visual descriptor is suitable for contour based object detection, because it encodes the sign information and can implicitly represent the global contour. When CENTRIST and linear classifier are used, we propose a computational method that does not need to explicitly generate feature vectors. It involves no image preprocessing or feature vector normalization, and only requires O(1) steps to test an image patch. C 4 is also friendly to further hardware acceleration. It has been applied to detect objects such as pedestrians, faces, and cars on benchmark data sets. It has comparable detection accuracy with state-of-the-art methods, and has a clear advantage in detection speed.

Original languageEnglish (US)
Article number6544207
Pages (from-to)4096-4107
Number of pages12
JournalIEEE Transactions on Image Processing
Issue number10
StatePublished - 2013
Externally publishedYes


  • Object detection
  • real-time

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design


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