Pedestrian detection in uncontrolled environments using stereo and biometric information

A method for pedestrian detection from challenging real world outdoor scenes is presented in this paper. This technique is able to extract multiple pedestrians, of varying orientations and appearances, from a scene even when faced with large and multiple occlusions. The technique is also robust to changing background lighting conditions and effects, such as shadows. The technique applies an enhanced method from which reliable disparity information can be obtained even from untextured homogeneous areas within a scene. This is used in conjunction with ground plane estimation and biometric information,to obtain reliable pedestrian regions. These regions are robust to erroneous areas of disparity data and also to severe pedestrian occlusion, which often occurs in unconstrained scenarios

Robust pedestrian detection and tracking in crowded scenes

In this paper, a robust computer vision approach to detecting and tracking pedestrians in unconstrained crowded scenes is presented. Pedestrian detection is performed via a 3D clustering process within a region-growing framework. The clustering process avoids using hard thresholds by using bio-metrically inspired constraints and a number of plan view statistics. Pedestrian tracking is achieved by formulating the track matching process as a weighted bipartite graph and using a Weighted Maximum Cardinality Matching scheme. The approach is evaluated using both indoor and outdoor sequences, captured using a variety of different camera placements and orientations, that feature significant challenges in terms of the number of pedestrians present, their interactions and scene lighting conditions. The evaluation is performed against a manually generated groundtruth for all sequences. Results point to the extremely accurate performance of the proposed approach in all cases

Event detection in pedestrian detection and tracking applications

In this paper, we present a system framework for event detection in pedestrian and tracking applications. The system is built upon a robust computer vision approach to detecting and tracking pedestrians in unconstrained crowded scenes. Upon this framework we propose a pedestrian indexing scheme and suite of tools for detecting events or retrieving data from a given scenario

A framework for evaluating stereo-based pedestrian detection techniques

Automated pedestrian detection, counting, and tracking have received significant attention in the computer vision community of late. As such, a variety of techniques have been investigated using both traditional 2-D computer vision techniques and, more recently, 3-D stereo information. However, to date, a quantitative assessment of the performance of stereo-based pedestrian detection has been problematic, mainly due to the lack of standard stereo-based test data and an agreed methodology for carrying out the evaluation. This has forced researchers into making subjective comparisons between competing approaches. In this paper, we propose a framework for the quantitative evaluation of a short-baseline stereo-based pedestrian detection system. We provide freely available synthetic and real-world test data and recommend a set of evaluation metrics. This allows researchers to benchmark systems, not only with respect to other stereo-based approaches, but also with more traditional 2-D approaches. In order to illustrate its usefulness, we demonstrate the application of this framework to evaluate our own recently proposed technique for pedestrian detection and tracking

Influence of blade aerodynamic model on prediction of helicopter rotor aeroacoustic signatures

Brown’s vorticity transport model has been used to investigate how the local blade aerodynamic model influences the quality of the prediction of the high-frequency airloads associated with blade–vortex interactions, and thus the accuracy with which the acoustic signature of a helicopter rotor can be predicted. The vorticity transport model can accurately resolve the structure of the wake of the rotor and allows significant flexibility in the way that the blade loading can be represented. The Second Higher-Harmonic Control Aeroacoustics Rotor Test was initiated to provide experimental insight into the acoustic signature of a rotor in cases of strong blade–vortex interaction. Predictions of two models for the local blade aerodynamics are compared with the test data. A marked improvement in accuracy of the predicted high-frequency airloads and acoustic signature is obtained when a lifting-chord model for the blade aerodynamics is used instead of a lifting-line-type approach. Errors in the amplitude and phase of the acoustic peaks are reduced, and the quality of the prediction is affected to a lesser extent by the computational resolution of the wake, with the lifting-chord model producing the best representation of the distribution of sound pressure below the rotor

3D image analysis for pedestrian detection

A method for solving the dense disparity stereo correspondence problem is presented in this paper. This technique is designed specifically for pedestrian detection type applications. A new Ground Control Points (GCPs) scheme is introduced, using groundplane homography information to determine regions in which good GCPs are likely to occur. The method also introduces a dynamic disparity limit constraint to further improve GCP selection and dense disparity generation. The technique is applied to a real world pedestrian detection scenario with a background modeling system based on disparity and edges

Detecting shadows and low-lying objects in indoor and outdoor scenes using homographies

Many computer vision applications apply background suppression techniques for the detection and segmentation of moving objects in a scene. While these algorithms tend to work well in controlled conditions they often fail when applied to unconstrained real-world environments. This paper describes a system that detects and removes erroneously segmented foreground regions that are close to a ground plane. These regions include shadows, changing background objects and other low-lying objects such as leaves and rubbish. The system uses a set-up of two or more cameras and requires no 3D reconstruction or depth analysis of the regions. Therefore, a strong camera calibration of the set-up is not necessary. A geometric constraint called a homography is exploited to determine if foreground points are on or above the ground plane. The system takes advantage of the fact that regions in images off the homography plane will not correspond after a homography transformation. Experimental results using real world scenes from a pedestrian tracking application illustrate the effectiveness of the proposed approach

Analytical models and system topologies for remote multispectral data acquisition and classification

Simple analytical models are presented of the radiometric and statistical processes that are involved in multispectral data acquisition and classification. Also presented are basic system topologies which combine remote sensing with data classification. These models and topologies offer a preliminary but systematic step towards the use of computer simulations to analyze remote multispectral data acquisition and classification systems

The colour of life: novel visualisations of population Lifestyles

Colour permeates our daily lives, yet we rarely take notice of it. In this work we utilise the SenseCam (a visual lifelogging tool), to investigte the predominant colours in one million minutes of human life that a group of 20 individuals encounter throughout their normal daily activities. We also compare the colours that different groups of people are exposed to in their typical days. This information is presented in using a novel colour-wheel visualisation which is a new means of illustrating that people are exposed to bright colours over longer durations of time during summer months, and more dark colours during winter months