This paper presents a novel approach for pedestrian detection using oriented line scans of gradients computed from a gray level image. Three feature types are proposed that can be generated easily from oriented gradients and an effective use of integral lines and integral images. A scalable cascaded classifier is built by combining oriented gradients with the oriented line scan features in a boosting framework. The detector's performance is comparable to the state of the art results and achieves about 3 to 5 fps on 320 x 240 resolution images making the proposed method suitable for real time applications. Detector performance is also represented as PUR, percentage of uncertainty removed.

Metric learning methods, for person re-identification, estimate a scaling for distances in a vector space that is optimized for picking out observations of the same individual. This paper presents a novel approach to the pedestrian re-identification problem that uses metric learning to improve the state-of-the-art performance on standard public datasets. Very high dimensional features are extracted from the source color image. A first processing stage performs unsupervised PCA dimensionality reduction, constrained to maintain the redundancy in color-space representation. A second stage further reduces the dimensionality, using a Local Fisher Discriminant Analysis defined by a training set. A regularization step is introduced to avoid singular matrices during this stage. The experiments conducted on three publicly available datasets confirm that the proposed method outperforms the state-of-the-art performance, including all other known metric learning methods. Further-more, the method is an effective way to process observations comprising multiple shots, and is non-iterative: the computation times are relatively modest. Finally, a novel statistic is derived to characterize the Match Characteristic: the normalized entropy reduction can be used to define the 'Proportion of Uncertainty Removed' (PUR). This measure is invariant to test set size and provides an intuitive indication of performance.