This paper presents novel features for face detection in the paradigm of AdaBoost algorithm. Features are multi-dimensional histograms computed from a set of rectangles in the filtered images, and they represent marginal distributions of these rectangles. The filter banks consist of intensity, Laplacian of Gaussian (Difference of Gaussians), and Gabor filters, aiming at capturing spatial and fr...

Briefly, what approach or combination of approaches did you test in each of your submitted runs? • all runs: video decoding using ffmpeg library, sampling every 25th frame. • F_X_NO_TNO_1: standard SURF keypoint detection, bag-of-words using 4096 prototypes from videos, indexing and querying using Lemur. • F_X_NO_TNO_2: standard SURF keypoint detection, bag-of-words using 256 prototypes from qu...

Boosted cascade of simple features, by Viola and Jones, is one of the most famous object detection frameworks. However, it suffers from a lengthy training process. This is due to the vast features space and the exhaustive search nature of Adaboost. In this paper we propose GAdaboost: a Genetic Algorithm to accelerate the training procedure through natural feature selection. Specifically, we pro...

This paper describes a machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates. This work is distinguished by three key contributions. The first is the introduction of a new image representation called the “Integral Image” which allows the features used by our detector to be computed very quickly. The secon...