The Chamfer System

ABOUT THE CHAMFER SYSTEM : -

The system detects pedestrians in images using shape matching with distance transforms. Predefined templates represent different pedestrian appearances. Key visual features relevant to the task are extracted from the input image using feature extraction, such as edges and regions of interest. The distance transform assigns each pixel a value based on its distance from the nearest object boundary, providing a measure of how well it matches the object shape. The DT image of the template is computed and compared to the DT image of the input image using a distance metric to determine similarity. If the similarity exceeds a threshold, the system outputs the object location and size. This technique is robust to variations in scale, rotation, and appearance, making it useful for a range of object detection tasks.

shape matching using distance detection: -

The template T is transformed (e.g. translated) and positioned over the resulting DT image of I. The matching measure D(T, I) is determined by the pixel values of the DT image that lie under the data pixels of the transformed template. This matching measure provides a quantitative measure of how well the template matches the input image at a given location.

These pixel values form a distribution of distances of the template features to the nearest features in the image. The lower these distances are, the better the match between the image and the template at that location. There are a number of different matching measures that can be defined based on this distance distribution.

One possibility is to use the average distance to the nearest feature. This is known as the chamfer distance, which is the namesake of the proposed system. Other more robust (and costly) measures further reduce the effect of missing features (i.e. due to occlusion or segmentation errors) by using the average truncated distance or the f-th quantile value (the Harsdorf distance).

In practical applications, a template is considered matched at locations where the distance measure D(T,I) is below a user-supplied threshold. This threshold value can be adjusted to control the trade-off between detection accuracy and the number of false positives generated by the system.

FIRST IMAGE:- Detecting pedestrians in an image.

SECOND IMAGE: - Captured with templates .

THIRD IMAGE: - Matching template T and image I involves computing the feature image of I.

FOURTH IMAGE: - The distance transform to obtain a DT image.

Application :: Pedestrian detection: -

The Chamfer System is a computer vision technique that can be used for object detection and recognition in images. It involves finding the closest match between an object template and the edges or contours of an image.

In the given context, the Chamfer System was used for detecting pedestrians in images. This task is more challenging than traffic sign detection because pedestrians come in many different shapes and sizes, which means that the Chamfer System needs to be able to recognize a wider variety of shapes.

Additionally, pedestrians are less well-defined in images than traffic signs. Pedestrian contours may be less pronounced or obscured by other objects in the image, making it more difficult to accurately segment and identify them.

Overall, the detection of pedestrians using the Chamfer System requires a more sophisticated and complex approach than other types of object detection.

• Previous work on "looking at people" typically used a static camera and background subtraction for initial segmentation.

• A database of approximately 1250 pedestrian shapes was compiled and a four-level pedestrian hierarchy was created.

• The leaf level was removed to obtain a more compact representation of the shape distribution and enable generalization, resulting in a three-level hierarchy with approximately 900 templates per scale.

• Five scales were used to cover a 50% size variation range, and initial experiments on a few hundred pedestrian images resulted in a detection rate of 75-85% per image with a few false positives.

In the given context, the text is referring to the use of templates for detecting pedestrians in images. Templates are image patterns or shapes that are used to match against the image being analyzed in order to identify specific objects or features.

The text mentions that the templates used in the pedestrian detection system are typically found on trees or windows. This suggests that the templates may be silhouette-like shapes of pedestrians that are used to match against the image being analyzed.

The "flat-world assumption" refers to the assumption that the world being captured by the camera is flat, which allows for certain geometric calculations to be made. Using this assumption and knowledge about camera geometry, the system is able to set regions of interest for the template in the hierarchy. This means that certain areas of the image can be excluded from consideration, which speeds up the matching process greatly.

By setting these regions of interest, many erroneous template locations can be excluded from consideration before the matching process even begins. This improves the efficiency and accuracy of the system.

Finally, the text notes that the current pedestrian system runs at a rate of 2-5 Hz on a 600 MHz Pentium processor with MMX. This means that the system is able to analyze and detect pedestrians in images at a relatively fast rate, even on older or less powerful computer hardware.

Conclusion: -

The system for detecting pedestrians in images using templates and the Chamfer System. The system is designed to handle the challenges of pedestrian detection, which involves a greater variety of shapes and less pronounced contours compared to other objects such as traffic signs. The system uses a database of about 1250 pedestrian shapes and a four-level hierarchy to enable efficient matching. The flat-world assumption and knowledge of camera geometry are used to set regions of interest for the templates, which improves efficiency by excluding many erroneous template locations. The system is capable of running at a relatively fast rate on older or less powerful computer hardware. Overall, the system is a sophisticated and complex approach to pedestrian detection that is designed to deliver accurate and efficient results.