Project Idea | Motion detection using Background Subtraction Techniques

• Can eliminate noise in the sequence of frames effectively using suitable BGS methods.
• Can efficiently detect foreground provided alpha and threshold is fixed.
• Motions in different challenges can be detected by subtracting issues like dynamic background etc.

• Background initialization: The first aim to build a background model is to fix number of frames. This model can be designed by various ways (Gaussian, fuzzy etc.).
• Foreground detection: In the next frames, a comparison is processed between the current frame and the background model. This subtraction leads to the computation of the foreground of the scene.
• Background maintenance: During this detection process, images are also analyzed in order to update the background model learned at the initialization step, with respect to a learning rate. An object not moving during long time should be integrated in the background for example.

BG Subtraction Methods step by step

1. Adaptive BG Learning: In a simple way, this can be done by setting manually a static image that represents the background, and having no moving object
   • For each video frame, compute the absolute difference between the current frame and the static image.
   • If absolute difference exceeds threshold, frame is regarded as background, otherwise foreground.
2. Gaussian mixture model (GMM): In order to model a background which is dynamic texture(such as waves on the water or trees shaken by the wind), each pixel with a mixture of K Gaussian distribution is modeled.
   • For each video frames, find the probability of input pixel value x from current frame at time t being a background pixel is represented by the following mixture of Gaussian
   • A new pixel is checked against the exiting K Gaussian distributions, until a match is found.
   • If none of K distributions match the current pixel value, the least probable distribution is replaced with a distribution with the current value as its mean value.
   • If pixel values cannot match the background model distributions, they will be labeled “in motion”, otherwise background pixel.
3. Fuzzy Integral:: The background initialization is made by using the average of the N first video frames where objects are present. An update rule of the background image is necessary to adapt well the system over time to some environmental changes. For this, a selective maintenance scheme is adopted as follows:

      

   The fuzzy integrals aggregates nonlinearly the outcomes of all criteria.
   • The pixel at position(x, y)is considered as foreground if its Choquet integral value is less than a certain constant threshold which means that pixels at the same position in the background and the current images are not similar.
   • This a constant value depending on each video data set.

• Library: OpenCV
• Language: C++
• Environment: Visual Studio
• Hardware: 2.67 GHz Core i5 4 GB RAM

Diagram

Result

Conclusion

Project Link 

Video

Research

• https://hal.archives-ouvertes.fr/hal-00333086/document
• http://ieeexplore.ieee.org/document/7565562/
• http://vc.cs.nthu.edu.tw/home/paper/codfiles/whtung/200603141526/Improved_Adaptive_Gaussian_Mixture_Model_for_Background.PDF

Application: