blockmatching 1.2.1

Block Matching Algorithm

Block Matching Algorithm

Block Matching

According to CUEVAS et al. (2013) in a block matching (BM) approach:

"...image frames in a video sequence are divided into blocks. For each
block in the current frame, the best matching block is identified inside a
region of the previous frame, aiming to minimize the sum of absolute
differences..."

From the work of Perez et al. (2010):

"...pixel-specific motion vectors are determined by calculating the RMSE of
 the difference between two consecutive Kt*grids surrounding the considered
 pixel when the second grid is advected in the direction of a motion vector.
 The selected motion vector corresponds to the lowest RMSE. This process is
 repeated for each image pixel, and each pixel is assigned an individual
 motion vector. Future images are obtained by displacing the current image
 pixels in the direction of their motion vector. Future images are
 subsequently smoothed by averaging each pixel with its 8 surrounding
 neighbors..."

For example, considering a image, in t0 + k dt, with 9x9 pixels and a block grid with 3x3 pixels. The image bellow it is assumed that the central pixel C is surrounding by pixels A.

* * * * * * * * *
* * * * * * * * *
* * * * * * * * *
* * * A A A * * *
* * * A C A * * *
* * * A A A * * *
* * * * * * * * *
* * * * * * * * *
* * * * * * * * *

Now, for a image in time t0 + (k+1)dt, the value of block with the pixel C, in the image in t0 +kdt, is compared with values of piexls in the 9x9 window of the image in t0+(k+1)dt.

The most probable direction of the moviment of the pixel C, at t0 + (k+1)dt, is given by the position of the corresponding block with the lowest square mean error -SME (subtraction of the 3x3 subgrid) (e.g. KHAWASE et al. (2017)).

In the following example, the 3x3 block was in the initial position i=4, j=4. The new initial subblock with lowest is in i=7, j=7.

Initial position of 3x3 block in t0 + kdt:

* * * * * * * * *
* * * * * * * * *
* * * * * * * * *
* * * A A A * * *
* * * A C A * * *
* * * A A A * * *
* * * * * * * * *
* * * * * * * * *
* * * * * * * * *

The new position of 3x3 block in t0 + (i+1)dt:

* * * * * * * * *
* * * * * * * * *
* * * * * * * * *
* * * * * * * * *
* * * * * * * * *
* * * * * * * * *
* * * * * * A A A
* * * * * * A C A
* * * * * * A A A

The size of search window depend of the expected velocity of the block. For slow moviment, a window with size of 3 times can be considered.

Background subtraction algorithm.

The background and foreground subtraction algorithm is based on the work of Yi and Fan (2010):

"...based on running average background modeling and temporal difference method.Firstly, we utilize the running average method to dynamically updating the background image. Through using background subtraction, we get a foreground image. Secondly, we use temporal difference method to get a difference image..."

License

Developed by: E. S. Pereira. e-mail: [e-mail]

Copyright [2019] [E. S. Pereira]

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

   http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

Bibliography

CUEVAS, Erik et al. Block matching algorithm for motion estimation based on Artificial Bee Colony (ABC). Applied Soft Computing, v. 13, n. 6, p. 3047-3059, 2013.

KHAWASE, Sonam T. et al. An Overview of Block Matching Algorithms for Motion Vector Estimation. In: Proceedings of the Second International Conference on Research in Intelligent and Computing in Engineering, str. 2017. p. 217-222.

Perez, R. et al. Validation of short and medium term operational solar radiation forecasts in the US. Solar Energy, 84. 12. 2161-2172. 2010.

Yi, Zheng, and Fan Liangzhong. Moving object detection based on running average background and temporal difference. 2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering. IEEE, 2010.

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