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The Histogram of Oriented Gradients (HOG) is a widely used feature descriptor in computer vision, especially for object detection tasks such as pedestrian detection. Introduced by Navneet Dalal at CVPR 2005, HOG captures the distribution of gradient directions within local image regions, making it effective for describing shape and texture. When combined with a Support Vector Machine (SVM), HOG has proven to be highly efficient in detecting humans in images.
At its core, the HOG method relies on gradient statistics. Since gradients are most prominent at edges, they provide a strong indication of object boundaries. The algorithm works by first converting the image into grayscale, then computing the gradient magnitude and direction for each pixel. These gradients are then aggregated into histograms over small grid cells, typically 6x6 pixels in size. Each cell’s histogram is composed of 9 bins, representing different orientation angles.
To enhance robustness against lighting variations and shadows, these histograms are normalized across larger blocks of cells. For example, a block might consist of 3x3 cells, and the histograms from all cells in that block are combined and normalized. This normalization step ensures that the final feature vector remains consistent even under varying illumination conditions.
One of the key advantages of HOG is its invariance to geometric and optical deformations. By focusing on local gradient distributions, HOG maintains stability against minor changes in pose or scale. Additionally, it allows for some flexibility in human posture, as long as the overall shape remains upright.
The implementation process involves several steps:
1. **Grayscale Conversion**: Convert the input image to grayscale to simplify processing.
2. **Gamma Correction**: Adjust the image’s contrast using gamma compression to reduce the impact of lighting changes.
3. **Gradient Calculation**: Compute horizontal and vertical gradients using filters like [-1, 0, 1] and [1, 0, -1].
4. **Cell Division**: Divide the image into small cells, typically 6x6 pixels.
5. **Histogram Construction**: For each cell, create a histogram of gradient directions, weighted by the magnitude of the gradient.
6. **Block Normalization**: Group cells into blocks and normalize their histograms to improve robustness.
7. **Feature Vector Creation**: Concatenate all block features into a single vector for classification.
Each block contributes a feature vector, which is then used by a classifier like SVM to determine whether a region contains a pedestrian. The final HOG feature vector can be quite large, depending on the image size and block configuration.
For instance, in a 64x128 image, with 16x16 pixel cells and 2x2 blocks, the total number of features could reach around 3,780. This makes HOG particularly powerful for real-time applications where speed and accuracy are essential. Overall, HOG remains a foundational technique in computer vision due to its simplicity, efficiency, and effectiveness in capturing visual patterns.