Moving Objects Removal PROCESAMIENTO DE IMAGENES DIGITALES Group - - PowerPoint PPT Presentation

Moving Objects Removal

PROCESAMIENTO DE IMÂGENES DIGITALES Group Project

Andrea Migotto Fabio Falcioni Giacomo Santoni

INTRODUCTION

PROBLEM:

Be able to get a photo of a landscape or a monument or anything else without people or vehicles ruining the photo

IDEA:

Take multiple photos with a static background and process them to obtain a picture without unwanted

• bjects

RESOLUTION

• 1. Images acquisition
• 2. Difference between

images

• 3. Canny detector
• 4. Binarization of the

difference

• 5. Elaboration of the

binarized image

• 6. Connected components

identification

• 7. Correlation computation and

comparison

• 8. Final image updating
• 9. Second execution

These are the main steps of our program. In the next slide they have been schematically sketched for a better understanding.

RESOLUTION

Images acquisition

Two or more images can be selected for being processed using a normal pop-up dialog

• window. The more images are used, the easier it is to get a good final result. This is

because a portion of the image is less probable to be covered in all photos by some moving object.

Difference between images

The difference between two images is calculated to detect movements within the same

• scene. The regions that do not

change between the images are black while all the moving

• bjects are highlighted, in this

way it’s possible to find what should be removed.

Edge detection

The Canny filter is used to detect the edges of each moving object. An image processed by the Canny algorithm makes easier to search for individual

• bjects

in the image database. Searching for colour matching could lead to wrong results as the difference in absolute value has been used.

Binarization of the difference

The difference is transformed into a black and white image thanks to the binarization. It consists in a reduction of the information on it, as you move to a representation formed only by true and false (1 and 0 respectively). A low threshold is used

• n

the difference image to bring to 0 the background and to 1 all the differences.

Elaboration of the binarized image

Through some different morphological

• perations

and filters, the binarized image is processed to remove isolated white spots and to highlight moving

• bjects.

More precisely, a median filter, erosion, expansion and filling have been used to not consider small difference like those caused by small variation in the angle or in the brightness between the two images elaborated.

Connected components identification

The connected components

• f the mask are extracted

and their bounding boxes are identified in order to isolate and locate each moving object and store the information about its position in the image. Each component thus contains an object that now has to be recodgnize in one of the two initial images.

Correlation comparison

For each connected component, in the corresponding region the correlation between the difference image and both the initial one and the provisional final one is computed and then compared. The object is then located in the image showing the higher correlation, while the other will contain the background of that part.

Final image

The final image is then updated by replacing the unwanted objects with the corresponding background

• f the other image. Clearly

having fewer background differences between the various images lead to a better result. Background differences, for example, include changes in light, shadow

• r

small frame variations between shots

Second execution

The result may be improved by choosing a “good” initial

• image. To do so each image
• f the set is compared with

the final one that we just

• btained

and the set is sorted in

• rder
• f

descending correlation, then the algorithm is executed

• again. In practice this means

choosing the image with fewer moving

• bjects

to make as few changes as possible to the image.

EXPERIMENTATION

The resolution was done with a set of only two images to better explain the program. The script can also work with a larger datastore formed by many initial images in which there are numerous moving objects. In the following there are some examples with shots taken in Seville:

• Municipio
• Plaza de España
• Prado de San Sebastián
• Paseo del Rio
• Real Alcazar
• Campus Reina Mercedes

Municipio – Initial images

Municipio – Final image

Plaza de España – Initial images

Plaza de España – Final image

Prado de San Sebastián – Initial image

Prado de San Sebastián – Final images

Paseo del Rio – Initial Images

Paseo del Rio – Final Images

Real Alcazar – Initial images

Real Alcazar – Final image

Campus Reina Mercedes – Initial images

Campus Reina Mercedes – Final image

CONCLUSIONS

After an exhaustive test phase with sets of pictures shot in different conditions, we can affirm that our method works well in real world situations and it’s rather versatile and can be implemented in a light and performing software. The vast majority of the moving objects, in particular we are interested in people, are correctly removed and the appearance of artifacts in the final image is minimal, if not inexistent. In conclusion, we can see that our method has its strengths and weaknesses, and presents interesting possibilities of improvement while it can already be used as a fully functional tool.

POSSIBLE IMPROVEMENTS

Using more sophisticated and refined techniques during the processing of the images and the correlation computation will allow to achieve better results Implement the possibility of using multiple shots even if they are not aligned using the SURF algorithm for features detection and matching The code can be optimized to achieve better computational speed and use less resources

Moving Objects Removal

PROCESAMIENTO DE IMÂGENES DIGITALES

THANKS FOR YOUR ATTENTION