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Discussed in Section 4.5.1, test data samples have been collected and stored as CSV files. Test data consists of the same lighting scenes as training, however input image frames have been refreshed and the lighting subtly adjusted. This provides different data for the system to test itself, yet still within the same scenarios as explained in Section 4.3.2. This is the same data that will be sent to the Waikato Environment for Knowledge Analysis (WEKA) program.

4.8

Summary

Proposed is a new concept to create classifiers that are based on multiple lighting scenarios.

The exposure setting process for each camera ensures different cameras focus on different areas with minimal overlap. The use of different lighting scenarios ensure the pie slice colour space descriptors generate a spread of data points, while assuming a normally distributed dataset ensures a simple normal density function may prioritise multiple matches if required.

In the next chapter, the MIDECC system is implemented and compared against common machine learning algorithms.

Chapter 5

5.

Experimental Setup

Experiments were performed using three cameras mounted above a robot soccer playing field in a computer laboratory with controlled lighting. Eight target colours spread across eleven regions are assessed at a capture resolution of 800 x 600 (WVGA). The eleven regions were strategically positioned across the field to ensure a spread of differing illumination conditions. The lighting in the room can be controlled to a repeatable level, matching the discussed lighting scenarios in the previous chapters.

5.1

Environment Setup

The environment choice for testing the proposed system is significant as its’ results will help determine the scalability and uniformity of results. Consumer electronics were used where appropriate to ensure the results weren’t subject to specialised hardware.

5.1.1

Cameras

Three Logitech QuickCam Pro 9000 web cameras were used as inputs for the system. The auto focus feature was enabled on power up only, to ensure a focused input image. Auto exposure was disabled, as the exposure setting process manually sets this value. The cameras were run at WVGA (800x600) resolution as opposed to their native 2- megapixel rating as three cameras at WVGA resolution met the limit of the Universal Serial Bus (USB) connection to the processing computer. Commands are sent to the cameras via the ‘Video 4 Linux 2’ (vl42) command line interface, enabling the system to command the cameras directly.

Figure 5-1: Logitech Camera locations, emphasis added

5.1.1.1

Locations

The three cameras were located approximately 2.5 meters above a robot soccer field, centred length-wise and spread along the fields’ width. The two cameras near each edge of the field are angled towards the centre at approximately 25 degrees. Mentioned previously, this layout is used to minimise any reflective properties of the playing field reflecting the harsh halogen light back into the camera lenses.

5.1.1.2

Transformations

Removing the perspective angle and aligning the three input images is crucial for pixel-to-pixel comparisons. If the images were not aligned, one pixel, which is at position (150,250) to one camera, may be at location (154,248) on another for example. This can result in many conflicts where one camera is at an edge of a colour patch, while another is on the black background. In the worst case scenario, this would severely negatively affect training and exposure adjustments.

The Open Computer Vision (OpenCV) library is used for image transformation, as it is optimised at performing image warping and homographic isomorphic adjustments. Firstly, the edges of the playing field are set manually by selection on the camera input image. This sets four ‘perspective’ points from which the homography matrix may be calculated using the findHomography() function. This function returns an adjustment matrix, which removes the image perspective when passed to the OpenCV warpPerspective() function.

Figure 5-2: Camera Raw Input images with red outline around focus plane (top), same images with pre-processing completed (bottom)

Secondly, the images are stretched slightly to match a standard aspect ratio. This ensures that once the homography warping is complete, squares remain as squares while the central playing field circle, used in the next processing step, also remains circular. The aspect ratio is calculated by the real-life playing field measurements, using the camera in the centre of the playing field as a reference.

Lastly, the three images are shifted on the X and Y axis to align the centre circle on the playing field. This process uses the houghCircles() function to find the central white circle on the playing field. The location differences are then calculated and saved with each camera calibration file, resulting in faster future processing.

Table 5-1: Camera Alignment values

Camera A Reference Camera

Camera B +2 on X-Axis, +9 on Y-Axis

Figure 5-3: Camera Alignment testing output

The alignment test image above, appears as a single circle cropped from the centre of the input images. This image actually has 33% opacity from each camera, blended together to give a preview of the alignment results. An image with no overlap or visible ‘ghosting’ around the white central lines means each camera is successfully aligned.