CERES :: Browsing by Author "Breckon, Toby P."

Browsing by Author "Breckon, Toby P."

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Open Access
A 3D extension to cortex like mechanisms for 3D object class recognition
(2012-06-21T00:00:00Z) Flitton, Greg T.; Breckon, Toby P.; Megherbi, Najla
We introduce a novel 3D extension to the hierarchical visual cortex model used for prior work in 2D object recognition. Prior work on the use of the visual cortex standard model for the explicit task of object class recognition has solely concentrated on 2D imagery. In this paper we discuss the explicit 3D extension of each layer in this visual cortex model hierarchy for use in object recognition in 3D volumetric imagery. We apply this extended methodology to the automatic detection of a class of threat items in Computed Tomography (CT) security baggage imagery. The CT imagery suffers from poor resolution and a large number of artefacts generated through the presence of metallic objects. In our examination of recognition performance we make a comparison to a codebook approach derived from a 3D SIFT descriptor and demonstrate that the visual cortex method out-performs in this imagery. Recognition rates in excess of 95% with minimal false positive rates are demonstrated in the detection of a range of threat items
Open Access
Adaptive object placement for augmented reality use in driver assistance systems
(2011-11-17T00:00:00Z) Bordes, Lucie; Breckon, Toby P.; Katramados, Ioannis; Kheyrollahi, Alireza
We present an approach for adaptive object placement for Augmented Reality (AR) use in driver assistance systems. Combined vanishing point and road surface detection enable the real-time adaptive emplacement of AR objects within a drivers' natural field of view for on-road information display. This work combines both automotive vision and multimedia production aspects of real-time visual engineering.
Open Access
Automatic Rain Drop Detection for Improved Sensing in Automotive Computer Vision Applications
(Cranfield University, 2014-04-04) Webster, Dereck D.; Breckon, Toby P.; Stillwell, Mark Lee
The presence of raindrop induced distortion can have a significant negative impact on computer vision applications. Here we address the problem of visual raindrop distortion in standard colour video imagery for use in non-static, automotive computer vision applications where the scene can be observed to be changing over subsequent consecutive frames. We utilise current state of the art research conducted into the investigation of salience mapping as means of initial detection of potential raindrop candidates. We further expand on this prior state of the art work to construct a combined feature rich descriptor of shape information (Hu moments), isolation of raindrops pixel information from context, and texture (saliency derived) within an improved visual bag of words verification framework. Support Vector Machine and Random Forest classification were utilised for verification of potential candidates, and the effects of increasing discrete cluster centre counts on detection rates were studied. This novel approach of utilising extended shape information, isolation of context, and texture, along with increasing cluster counts, achieves a notable 13% increase in precision (92%) and 10% increase in recall (86%) against prior state of the art. False positive rates were also observed to decrease with a minimal false positive rate of 14% observed.
Open Access
Automatic road environment classification
(Institute of Electrical and Electronics Engineers Inc, 2011-06-30T00:00:00Z) Tang, Isabelle; Breckon, Toby P.
The ongoing development autonomous vehicles and adaptive vehicle dynamics present in many modern vehicles has generated a need for road environment classification - i.e., the ability to determine the nature of the current road or terrain environment from an onboard vehicle sensor. In this paper, we investigate the use of a low-cost camera vision solution capable of urban, rural, or off-road classification based on the analysis of color and texture features extracted from a driver's perspective camera view. A feature set based on color and texture distributions is extracted from multiple regions of interest in this forward-facing camera view and combined with a trained classifier approach to resolve two road-type classification problems of varying difficulty - {off-road, on-road} environment determination and the additional multiclass road environment problem of {off-road, urban, major/trunk road and multilane motorway/carriageway}. Two illustrative classification approaches are investigated, and the results are reported over a series of real environment data. An optimal performance of ~90% correct classification is achieved for the {off-road, on-road} problem at a near real-time classification rate of 1 Hz.
Open Access
A comparison of 3D interest point descriptors with application to airport baggage object detection in complex CT imagery
(Elsevier, 2013-02-16) Flitton, Greg T.; Breckon, Toby P.; Megherbi, Najla
We present an experimental comparison of 3D feature descriptors with application to threat detection in Computed Tomography (CT) airport baggage imagery. The detectors range in complexity from a basic local density descriptor, through local region histograms and three-dimensional (3D) extensions to both to the RIFT descriptor and the seminal SIFT feature descriptor. We show that, in the complex CT imagery domain containing a high degree of noise and imaging artefacts, a specific instance object recognition system using simpler descriptors appears to outperform a more complex RIFT/SIFT solution. Recognition rates in excess of 95% are demonstrated with minimal false-positive rates for a set of exemplar 3D objects.
Open Access
A comparison of classification approaches for threat detection in CT based baggage screening
(IEEE, 2013-02-21) Megherbi, Najla; Han, Jiwan; Breckon, Toby P.; Flitton, Greg T.
Computed Tomography (CT) based baggage security screening systems are of increasing use in transportation security. The ability to automatically identify potential threat item is a key aspect of current research in this area. Here we present a comparison of varying classification approaches for the automated detection of threat objects in cluttered 3D CT imagery from such security screening systems. By combining 3D medical image segmentation techniques with 3D shape classification and retrieval methods we compare five varying final classification stage approaches and present significant performance achievements in the automated detection of specified exemplar items.
Open Access
Correlating histology and spectroscopy to differentiate pathologies of the colon
(2012-09-25) Carey, Duane; Shepherd, Neil A.; Kendall, Catherine; Stone, Nick; Breckon, Toby P.; Lloyd, Gavin Rhys ; Xianghua, Xie
The techniques and procedures associated with histology are, in most cases, suitable for the diagnosis of colonic carcinomas. However, in cases such as epithelial misplacement the morphology of a stained tissue sample is homologous to that of cancer. This can lead to patients being misdiagnosed and undergoing unnecessary surgery. To prevent this surgery we suggest that the epithelium of tissue samples be examined using infrared (IR) spectroscopy. In this study, IR maps of tissue sections were registered to standard histology images so that epithelial specific spectra could be collected. The differences between these spectra were explored by using Principal Component Analysis (PCA). This paper provides a novel protocol detailing how histology specific spectra can be collected. The potential usefulness of these spectra is demonstrated through the separation of epithelial misplacement cases and colonic carcinomas within PCA space.
Open Access
The development of novel adjuncts to aid in the diagnosis of Epithelial Misplacement
(Cranfield University, 2013-06) Carey, Duane Owen; Kendall, Catherine; Breckon, Toby P.; Shepherd, Neil A.; Stone, Nicholas
Epithelial Misplacement (EM) is a benign phenomenon that occurs within polyps most commonly associated with the sigmoid colon. It is brought about because of the colons convulsive nature and this forces a polyps surface epithelium into its submucosa and also causes bleeding. This is problematic as the Bowel Cancer Screening Programme (BCSP) uses positive Faecal Occult Blood (FOB) test results to identify patients that require pathological review. As EM polyps bleed, they get selected for assessment and this results in them being sectioned and stained. In these cross sections, submucosal glandular tissue will be found that looks like it has formed due to metastatic mechanisms. This can lead to ambiguous diagnoses that will cause some patients to undergo unnecessary surgery. It is postulated that this can be prevented if the continuity of the EM samples could be measured. This is because only in the EM cases will the submucosal epithelial tissue remain in continuity with the surface. To test this, volumes representative of 9 samples of cancer and 13 cases of EM were segmented and their number of 26 three dimensional (3D) connected components were recorded. These were used with the 99% confidence limits of the two tailed Mann Whitney U Statistic and tested the null hypothesis that the cancer cases were as connected as the EM samples. In this instance, no significant differences were found and so the benefit of measuring the connectivity of these pathologies is questionable. It was because of this that Immunohistochemical (IHC) alternatives were considered. It was found that Collagen IV antibody staining correctly differentiated nine samples of EM from ten cases of cancer. The Mann Whitney U Statistic found this to be highly significant, p < 0.001, and future investigations should concentrate on automating this analysis. Although, Collagen IV provided a good classification it relied upon the subjective assessment of a pathologist. Therefore, the use of epithelial specific IR spectra was also investigated and this enabled the eleven EM and nine cancer cases that were investigated to be accurately classified 80% of the time upon cross validation. The collection of epithelial specific spectra relied upon a novel digital staining technique that has much application within future research. This study demonstrates that the intermodal registration of complementary modalities is of benefit to the disease classification problem. This technique has potential to be used in the correct identification of EM but more work is required.
Open Access
Distributed scene reconstruction from multiple mobile platforms
(Cranfield University, 2015-05) Cavestany, Pedro; Breckon, Toby P.; Martinez-Barbera, Humberto
Recent research on mobile robotics has produced new designs that provide house-hold robots with omnidirectional motion. The image sensor embedded in these devices motivates the application of 3D vision techniques on them for navigation and mapping purposes. In addition to this, distributed cheapsensing systems acting as unitary entity have recently been discovered as an efficient alternative to expensive mobile equipment. In this work we present an implementation of a visual reconstruction method, structure from motion (SfM), on a low-budget, omnidirectional mobile platform, and extend this method to distributed 3D scene reconstruction with several instances of such a platform. Our approach overcomes the challenges yielded by the plaform. The unprecedented levels of noise produced by the image compression typical of the platform is processed by our feature filtering methods, which ensure suitable feature matching populations for epipolar geometry estimation by means of a strict quality-based feature selection. The robust pose estimation algorithms implemented, along with a novel feature tracking system, enable our incremental SfM approach to novelly deal with ill-conditioned inter-image configurations provoked by the omnidirectional motion. The feature tracking system developed efficiently manages the feature scarcity produced by noise and outputs quality feature tracks, which allow robust 3D mapping of a given scene even if - due to noise - their length is shorter than what it is usually assumed for performing stable 3D reconstructions. The distributed reconstruction from multiple instances of SfM is attained by applying loop-closing techniques. Our multiple reconstruction system merges individual 3D structures and resolves the global scale problem with minimal overlaps, whereas in the literature 3D mapping is obtained by overlapping stretches of sequences. The performance of this system is demonstrated in the 2-session case. The management of noise, the stability against ill-configurations and the robustness of our SfM system is validated on a number of experiments and compared with state-of-the-art approaches. Possible future research areas are also discussed.
Open Access
Effective temporal change detection in low altitude aerial imagery: using 3D structure and colour to detect scene change in models generated from 2D imagery.
(Cranfield University, 2019-12) Richardson, Alex; Breckon, Toby P.; Zhao, Yifan
Unmanned Aerial Vehicles (UAVs) are now common place and their sensor solutions are producing ever increasing volumes of data. Typically the data is based around the theme of remote sensing of the Earth, and is gathered by a multitude of sensors for differing applications. The requirement to process the data gathered into useful information grows as does the demand for intelligent systems to assist with this. The most common, cost effective and readily available sensor solution is through standard camera photography, and offers the most usable data format without specialist tools. This also allows for proven methods to process the data gathered by a UAV thorough image processing and computation vision. One consistent theme in computer vision research is the drive for the ability to accurately reconstruct 3D scenes from 2D imagery through the process of Structure from Motion (SfM). This thesis details the research into the use of this 3D imagery, specifically aiding the ability to detect temporal change in dynamic scenes. This work presents a new technique to increase probability of detection and reduce computation required for such a process, the 3D Structure and Colour (3DSAC) differencing technique. The technique also goes to present a visualisation ability that best uses the algorithm for additional end user analysis beyond that of mathematics. Three scenarios where complex non-uniform changes are presented, of which assess and validate this technique to offer a capability to cope with dynamic scenes. The weighted 3DSAC algorithm gives the end user the ability to configure with emphasis being placed more within either structural or colour changes. Finally, through the implementation and evaluation of other current state of the art techniques for describing 3D points, the research shows the 3DSAC technique is more performant with imagery gathered by low altitude UAVs.
Open Access
An empirical comparison of real-time dense stereo approaches for use in the automotive environment
(Springer, 2012-08-16) Mroz, Filip; Breckon, Toby P.
In this work we evaluate the use of several real-time dense stereo algorithms as a passive 3D sensing technology for potential use as part of a driver assistance system or autonomous vehicle guidance. A key limitation in prior work in this area is that although significant comparative work has been done on dense stereo algorithms using de facto laboratory test sets only limited work has been done on evaluation in real world environments such as that found in potential automotive usage. This comparative study aims to provide an empirical comparison using automotive environment video imagery and compare this against dense stereo results drawn on standard test sequences in addition to considering the computational requirement against performance in real-time. We evaluate five chosen algorithms: Block Matching, Semi-Global Matching, No-Maximal Disparity, Cross-Based Local Approach, Adaptive Aggregation with Dynamic Programming. Our comparison shows a contrast between the results obtained on standard test sequences and those for automotive application imagery where a Semi-Global Matching approach gave the best empirical performance. From our study we can conclude that the noise present in automotive applications, can impact the quality of the depth information output from more complex algorithms (No-Maximal Disparity, Cross-Based Local Approach, Adaptive Aggregation with Dynamic Programming) resulting that in practice the disparity maps produced are comparable with those of simpler approaches such as Block Matching and Semi-Global Matching which empirically perform better in the automotive environment test sequences. This empirical result on automotive environment data contradicts the comparative result found on standard dense stereo test sequences using a statistical comparison methodology leading to interesting observations regarding current relative evaulation approaches.
Open Access
An experimental survey of metal artefact reduction in computed tomography
(Elsevier Science B.V., Amsterdam, 2013-11-01T00:00:00Z) Mouton, Andre; Megherbi Bouallagui, Najla; Van Slambrouck, Katrien; Nuyts, Johan; Breckon, Toby P.
We present a survey of techniques for the reduction of streaking artefacts caused by metallic objects in X-ray Computed Tomography (CT) images. A comprehensive review of the existing state-of- the-art Metal Artefact Reduction (MAR) techniques, drawn almost exclusively from the medical CT literature, is supported by an experimental comparison grounded in an evaluation based on a standard scienti c comparison protocol for MAR methods using a software generated medical phan- tom image. This experimental comparison is further extended by considering novel applications of CT imagery consisting of isolated metal objects with no surrounding tissue, as is encountered in typical engineering and security screening CT applications. We nd that the performance of twelve state-of-the-art MAR techniques to be fairly consistent across the two domains and demonstrate the feasibility of a reference-free quantitative performance measure. The literature review and experi- mentation demonstrate several trends. In particular, the major limitations of state-of-the-art MAR techniques are a dependence on prior knowledge, a sensitivity to input parameters and a shortage of comprehensive performance analyses. This study thus extends previous works by: comparing several state-of-the-art MAR techniques; considering both medical and non-medical applications and performing a comprehensive quantitative analysis, taking into account image quality as well as computational requirements.
Open Access
Extending computer vision techniques to recognition problems in 3d volumetric baggage imagery
(Cranfield University, 2012-01) Flitton, Greg T.; Breckon, Toby P.
We investigate the application of computer vision techniques to rigid object recognition in Computed Tomography (CT) security scans of baggage items. This imagery is of poor resolution and is complex in nature: items of interest can be imaged in any orientation and copious amounts of clutter, noise and artefacts are prevalent. We begin with a novel 3D extension to the seminal SIFT keypoint descriptor that is evaluated through specific instance recognition in the volumetric data. We subsequently compare the performance of the SIFT descriptor against a selection of alternative descriptor methodologies. We demonstrate that the 3D SIFT descriptor is notably outperformed by simpler descriptors which appear to be more suited for use in noise and artefact-prone CT imagery. Rigid object class recognition in 3D volumetric baggage data has received little attention in prior work. We evaluate contrasting techniques between a traditional approach derived from interest point descriptors and a novel technique based on modelling of the primary components of the primate visual cortex. We initially demonstrate class recognition through the implementation of a codebook approach. A variety of aspects relating to codebook generation are investigated (codebook size, assignment method) using a range of feature descriptors. Recognition of a number of object classes is performed and results from this show that the choice of descriptor is a critical aspect. Finally, we present a unique extension to the established standard model of the visual cortex: a volumetric implementation. The visual cortex model comprises a hierarchical structure of alternating simple and complex operations that has demonstrated excellent class recognition results using 2D imagery. We derive 3D extensions to each layer in the hierarchy resulting in class recognition results that signficantly outperform those achieved using the earlier traditional codebook approach. Overall we present several novel solutions to object recognition within 3D CT security images that are supported by strong statistical results.
Open Access
A hierarchical approach to 3D non-parametric surface relief completion
(Elsevier Science B.V., Amsterdam., 2012-01-01T00:00:00Z) Breckon, Toby P.; Fisher, Robert B.
Typical stereo and laser scan based 3D acquisition approaches are essentially limited to 2.5D capture. The resulting 3D completion problem, to derive missing information in 2.5D scenes from limited contextual information, has received increasing attention in literature. Here we propose a hierarchical extension to our recent non-parametric approach for the 3D completion of surface relief detail to allow the resolution of inconsistencies arising in the global structure of an area completed with this technique. We test our approach over a range of surface types and contrast the presence of global discontinuities in the resulting completion with those of the earlier approach.
Open Access
Improved depth recovery in consumer depth cameras via disparity space fusion within cross-spectral stereo
(British Machine Vision Association, 2014-09-01) Payen de La Garanderie, Gregoire; Breckon, Toby P.
We address the issue of improving depth coverage in consumer depth cameras based on the combined use of cross-spectral stereo and near infrared structured light sensing. Specifically we show that fusion of disparity over these modalities prior to subsequent optimization, within the disparity space image, facilitates the recovery of scene depth information in regions where structured light sensing alone fails. This joint approach, leveraging disparity information from both structured light and cross-spectral stereo, facilitates the recovery of global scene depth comprising both texture-less object depth, where stereo sensing commonly fails, and highly reflective object depth, where structured light active sensing commonly fails. The proposed solution is illustrated using dense gradient feature matching and is shown to outperform prior approaches that use late-stage fused cross-spectral stereo depth as a facet of improved sensing for consumer depth cameras.
Open Access
A non-temporal texture driven approach to real-time fire detection
(2011-09-14T00:00:00Z) Chenebert, Audrey; Breckon, Toby P.; Gaszczak, Anna
Here we investigate the automatic detection of fire pixel regions in conventional video (or still) imagery within realtime bounds. As an extension to prior, established approaches within this field we specifically look to extend the primary use of threshold-driven colour spectroscopy to the combined use of colour-texture feature descriptors as an input to a trained classification approach that is independent of temporal information. We show the limitations of such spectroscopy driven approaches on simple, real-world examples and propose our novel extension as a robust, real-time solution within this field by combining simple texture descriptors to illustrate maximal ∼98% fire region detecti
Open Access
A novel intensity limiting approach to Metal Artefact Reduction in 3D CT baggage imagery
(IEEE, 2013-02-21) Mouton, Andre; Megherbi, Najla; Flitton, Greg T.; Bizot, Suzanne; Breckon, Toby P.
This paper introduces a novel technique for Metal Artefact Reduction (MAR) in the previously unconsidered context 3D CT baggage imagery. The output of a conventional sinogram completion-based MAR approach is refined by imposing an upper limit on the intensity of the corrected images and by performing post-filtering using the non-local means filter. Furthermore, performance is evaluated using a novel quantitative analysis technique, using the ratio of noisy 3D SIFT detection points identified, as well as a standard qualitative comparison (visual quality). The objective of the quantitative analysis is to evaluate the impact of MAR on the application of computer vision techniques for automatic object recognition. The study yields encouraging results in both the qualitative and quantitative analyses. The proposed method yields a significant improvement in performance when compared to algorithms based on linear interpolation and reprojection-reconstruction; especially in terms of reducing the occurrence of new artefacts in the corrected images. The results serve as a strong indication that MAR will aid human and computerised analyses of 3D CT baggage imagery for transport security screening.
Open Access
On artefact reduction, segmentation and classification of 3D computed tomography imagery in baggage security screening
(Cranfield University, 2014-03) Mouton, Andre; Breckon, Toby P.; Armitage, Carol
This work considers novel image-processing and computer-vision techniques to advance the automated analysis of low-resolution, complex 3D volumetric Computed Tomography (CT) imagery obtained in the aviation-security-screening domain. Novel research is conducted in three key areas: image quality improvement, segmentation and classification. A sinogram-completion Metal Artefact Reduction (MAR) technique is presented. The presence of multiple metal objects in the scanning Field of View (FoV) is accounted for via a distance-driven weighting scheme. The technique is shown to perform comparably to the state-of-the-art medical MAR techniques in a quantitative and qualitative comparative evaluation. A materials-based technique is proposed for the segmentation of unknown objects from low-resolution, cluttered volumetric baggage-CT data. Initial coarse segmentations, generated using dual-energy techniques, are refined by partitioning at automatically-detected regions. Partitioning is guided by a novel random-forestbased quality metric (trained to recognise high-quality, single-object segments). A second segmentation-quality measure is presented for quantifying the quality of full segmentations. In a comparative evaluation, the proposed method is shown to produce similar-quality segmentations to the state-of-the-art at reduced processing times. A codebook model constructed using an Extremely Randomised Clustering (ERC) forest for feature encoding, a dense-feature-sampling strategy and a Support Vector Machine (SVM) classifier is presented. The model is shown to offer improvements in accuracy over the state-of-the-art 3D visual-cortex model at reduced processing times, particularly in the presence of noise and artefacts. The overall contribution of this work is a novel, fully-automated and effcient framework for the classification of objects in cluttered 3D baggage-CT imagery. It extends the current state-of-the-art by improving classification performance in the presence of noise and artefacts; by automating the previously-manual isolation of objects and by decreasing processing times by several orders of magnitude.
Open Access
Quantitative evaluation of stereo visual odometry for autonomous vessel localisation in inland waterway sensing applications
(MDPI, 2015-12) Kriechbaumer, Thomas; Blackburn, Kim; Breckon, Toby P.; Hamilton, Oliver; Rivas Casado, Monica
Autonomous survey vessels can increase the efficiency and availability of wide-area river environment surveying as a tool for environment protection and conservation. A key challenge is the accurate localisation of the vessel, where bank-side vegetation or urban settlement preclude the conventional use of line-of-sight global navigation satellite systems (GNSS). In this paper, we evaluate unaided visual odometry, via an on-board stereo camera rig attached to the survey vessel, as a novel, low-cost localisation strategy. Feature-based and appearance-based visual odometry algorithms are implemented on a six degrees of freedom platform operating under guided motion, but stochastic variation in yaw, pitch and roll. Evaluation is based on a 663 m-long trajectory (>15,000 image frames) and statistical error analysis against ground truth position from a target tracking tachymeter integrating electronic distance and angular measurements. The position error of the feature-based technique (mean of ±0.067 m) is three times smaller than that of the appearance-based algorithm. From multi-variable statistical regression, we are able to attribute this error to the depth of tracked features from the camera in the scene and variations in platform yaw. Our findings inform effective strategies to enhance stereo visual localisation for the specific application of river monitoring.
Open Access
Real-time object detection using monocular vision for low-cost automotive sensing systems
(Cranfield University, 2013-02) Katramados, Ioannis; Breckon, Toby P.
This work addresses the problem of real-time object detection in automotive environments using monocular vision. The focus is on real-time feature detection, tracking, depth estimation using monocular vision and finally, object detection by fusing visual saliency and depth information. Firstly, a novel feature detection approach is proposed for extracting stable and dense features even in images with very low signal-to-noise ratio. This methodology is based on image gradients, which are redefined to take account of noise as part of their mathematical model. Each gradient is based on a vector connecting a negative to a positive intensity centroid, where both centroids are symmetric about the centre of the area for which the gradient is calculated. Multiple gradient vectors define a feature with its strength being proportional to the underlying gradient vector magnitude. The evaluation of the Dense Gradient Features (DeGraF) shows superior performance over other contemporary detectors in terms of keypoint density, tracking accuracy, illumination invariance, rotation invariance, noise resistance and detection time. The DeGraF features form the basis for two new approaches that perform dense 3D reconstruction from a single vehicle-mounted camera. The first approach tracks DeGraF features in real-time while performing image stabilisation with minimal computational cost. This means that despite camera vibration the algorithm can accurately predict the real-world coordinates of each image pixel in real-time by comparing each motion-vector to the ego-motion vector of the vehicle. The performance of this approach has been compared to different 3D reconstruction methods in order to determine their accuracy, depth-map density, noise-resistance and computational complexity. The second approach proposes the use of local frequency analysis of i ii gradient features for estimating relative depth. This novel method is based on the fact that DeGraF gradients can accurately measure local image variance with subpixel accuracy. It is shown that the local frequency by which the centroid oscillates around the gradient window centre is proportional to the depth of each gradient centroid in the real world. The lower computational complexity of this methodology comes at the expense of depth map accuracy as the camera velocity increases, but it is at least five times faster than the other evaluated approaches. This work also proposes a novel technique for deriving visual saliency maps by using Division of Gaussians (DIVoG). In this context, saliency maps express the difference of each image pixel is to its surrounding pixels across multiple pyramid levels. This approach is shown to be both fast and accurate when evaluated against other state-of-the-art approaches. Subsequently, the saliency information is combined with depth information to identify salient regions close to the host vehicle. The fused map allows faster detection of high-risk areas where obstacles are likely to exist. As a result, existing object detection algorithms, such as the Histogram of Oriented Gradients (HOG) can execute at least five times faster. In conclusion, through a step-wise approach computationally-expensive algorithms have been optimised or replaced by novel methodologies to produce a fast object detection system that is aligned to the requirements of the automotive domain.