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.

In this paper, a system for automatic robust video surveillance is described and in particular its application to the problem of locating vehicles that stop in prohibited area is discussed. The structure of software for video processing (alarm generation, interface with the operator and information storage) is outlined together with the hardware (Trimedia DSP boards and industrial computers) which constitutes an industrial-grade product. The emphasis on this paper is to demonstrate robust detection and hence we show the results of a performance evaluation process carried out with the UK's i-LIDS "Parked Vehicle " reference dataset.

This paper presents a real time surveillance system that can be deployed within public transport environments. The system is a fully customisable application that can be used to address the requirements for this application domain. The system has been under trial on a number of metropolitan railway systems in the UK and Italy. In this paper we discuss how some of the algorithms employed within the system can be adapted for use with the CREDS datasets.

On-line surveillance to improve safety and security is a major requirement for the management of public transport networks and other public places. The surveillance task is a complex one involving people, management procedures, and technology. This work describes an architecture that takes into account the distributed nature of the detection processes and the need to allow for different types of devices and actuators. This was part of a major European initiative on intelligent transport systems. Because of the dominant nature of closed circuit television in surveillance, This work describes in detail a computer-vision module used in the system and its particular ability to detect situations of interest in busy conditions. The system components have been implemented, integrated, and tested in real metropolitan railway environments and are considered to be the first step toward providing ambient intelligence in such complex scenarios. Results are presented that not only deal with detection performance, but also on the perception of people who used the system on its effectiveness and potential impact.