Binary image filtering for object detection based on Haar feature density map

Chengqi Li; Zhigang Ren; Bo Yang

doi:10.1117/12.2300505

19 December 2017 Binary image filtering for object detection based on Haar feature density map

Chengqi Li, Zhigang Ren, Bo Yang

Proceedings Volume 10613, 2017 International Conference on Robotics and Machine Vision; 1061303 (2017) https://doi.org/10.1117/12.2300505
Event: Second International Conference on Robotics and Machine Vision, 2017, Kitakyushu, Japan

Abstract

The most concerned problem is to detect the interesting objects in image sequence captured from the same scene. Image difference is a commonly used method in detecting the interesting object, however, massive noise exists in the binarized difference image, so how to remove the noise is a hot issue. Aiming at the removing the noise in binary difference image, we propose a novel filtering algorithm based on Haar feature density map. Firstly, calculate the Haar feature density distribution map of binary image. Secondly, the density distribution map of Haar feature is binarized to remove noise. Finally, the interesting objects can be easily detected. Experiments show that the Haar feature density map achieves a better filtering effect than the conventional filtering algorithms for binary image (such as median filtering, morphological operation and so on).

Citation Download Citation

Chengqi Li, Zhigang Ren, and Bo Yang "Binary image filtering for object detection based on Haar feature density map", Proc. SPIE 10613, 2017 International Conference on Robotics and Machine Vision, 1061303 (19 December 2017); https://doi.org/10.1117/12.2300505

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