Adaptive Threshold for Background Subtraction in Moving Object Detection using Stationary Wavelet Transforms 2D

Both detection and tracking objects are challenging problems because of the type of the objects and even their presence in the scene. Generally, object detection is a prerequisite for target tracking, and tracking has no effect on object detection. In this paper, we propose an algorithm to detect and track moving objects automatically of a video sequence analysis, taken with a fixed camera. In the detection steps we perform a background subtraction algorithm, the obtained results are decomposed using discrete stationary wavelet transform 2D and the coefficients are thresholded using Birge-Massart strategy. The tracking step is based on the classical Kalman filter algorithm. This later uses the Kalman filter as many as the number of the moving objects in the image frame. The tests evaluation proved the efficiency of our algorithm for motion detection using adaptive threshold. The comparison results show that the proposed algorithm gives a better performance of detection and tracking than the other methods.

@article{Boufares2016,
  title     = {Adaptive Threshold for Background Subtraction in Moving Object Detection using Stationary Wavelet Transforms 2D},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {7},
  number    = {8},
  year      = {2016},
  publisher = {The Science and Information Organization},
  author    = {Oussama Boufares and Noureddine Aloui and Adnene Cherif},
  doi       = {10.14569/IJACSA.2016.070805},
  url       = {https://doi.org/10.14569/IJACSA.2016.070805}
}