What is an Effective Feature for a Detection Problem? Feature Evaluation in Multiple Scenes

We investigated effective features for human detection. The histogram of oriented gradients (HOG), which was proposed by N. Dalal, is an important representation that accumulates the edge-magnitude into a quantized histogram. Effective features similar to the HOG have been proposed. We question what the most effective feature is. We thus evaluate several features on three datasets of pedestrians, faces, and vehicles. We select the scale-invariant feature transform, local binary pattern, higher-order local auto correlation (HLAC), co-occurrence HOG, and extended CoHOG in addition to the HOG as features. These features have been adopted as effective features in related works. The features are applied to human detection on each dataset employing the real AdaBoost classifier. A comparison of classification results reveals that the combination of the HLAC and CoHOG is an effective feature for human detection.