Stereo-Based Pedestrian Detection for Collision-Avoidance Applications

Nedevschi, Sergiu; Bota, Silviu; Tomiuc, C.

doi:10.1109/tits.2008.2012373

Cited by 104 publications

(54 citation statements)

References 23 publications

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“…A Kalman-filter-based tracking algorith m [2] is used to track the strong pedestrian hypotheses. Morphological approach [11] is used to group the candidate points which are the potential parts of object chosen for grouping from the densities of disparities.…”

Section: Image Matching and Obs Tacle Detectionmentioning

confidence: 99%

Real Time Stereo Vision System for Safe Driving Distance Measurement-a Survey

2015

IJAERD

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-The paper presents a model of stereo vision system for intelligent vehicles fo r distance measurement to avoid forward car collision. The system consists of two cameras of parallel optical axes and separated horizontally fro m each other by a small d istance and these two cameras are co mbined together in a single frame. Many approaches have been proposed for effect ive distance measurement. In this paper, we have shown the detail survey on distance measurement of vehicle and obstacle using stereo vision system.

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Section: Image Matching and Obs Tacle Detectionmentioning

confidence: 99%

Real Time Stereo Vision System for Safe Driving Distance Measurement-a Survey

2015

IJAERD

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“…Cascaded architectures for pedestrian detection, involving modules using different cues to narrow down the image search space, have been prevalent (e.g., [15], [17], [30], and [31]). A recent trend involves the integration of multiple features (Haar wavelets, HOG, LRF, etc.)…”

Section: Previous Workmentioning

confidence: 99%

The Benefits of Dense Stereo for Pedestrian Detection

Keller

Enzweiler

Rohrbach

et al. 2011

IEEE Trans. Intell. Transport. Syst.

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Abstract-This paper presents a novel pedestrian detection system for intelligent vehicles. We propose the use of dense stereo for both the generation of regions of interest and pedestrian classification. Dense stereo allows the dynamic estimation of camera parameters and the road profile, which, in turn, provides strong scene constraints on possible pedestrian locations. For classification, we extract spatial features (gradient orientation histograms) directly from dense depth and intensity images. Both modalities are represented in terms of individual feature spaces, in which discriminative classifiers (linear support vector machines) are learned. We refrain from the construction of a joint feature space but instead employ a fusion of depth and intensity on the classifier level. Our experiments involve challenging image data captured in complex urban environments (i.e., undulating roads and speed bumps). Our results show a performance improvement by up to a factor of 7.5 at the classification level and up to a factor of 5 at the tracking level (reduction in false alarms at constant detection rates) over a system with static scene constraints and intensity-only classification.

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“…Most approaches follow a module-based strategy comprising generation of possible pedestrian location hypotheses (regions-of-interest, ROI), followed by pedestrian classification and tracking ( [3], [11], [18]). A detailed review of state-of-the-art pedestrian systems is beyond the scope of this paper.…”

Section: Previous Workmentioning

confidence: 99%

A new benchmark for stereo-based pedestrian detection

Keller

Enzweiler

Gavrila

2011

2011 IEEE Intelligent Vehicles Symposium (IV)

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Abstract-Pedestrian detection is a rapidly evolving area in the intelligent vehicles domain. Stereo vision is an attractive sensor for this purpose. But unlike for monocular vision, there are no realistic, large scale benchmarks available for stereobased pedestrian detection, to provide a common point of reference for evaluation. This paper introduces the Daimler Stereo-Vision Pedestrian Detection benchmark, which consists of several thousands of pedestrians in the training set, and a 27-min test drive through urban environment and associated vehicle data. The data, including ground truth, is made publicly available for non-commercial purposes. The paper furthermore quantifies the benefit of stereo vision for ROI generation and localization; at equal detection rates, false positives are reduced by a factor of 4-5 with stereo over mono, using the same HOG/linSVM classification component.

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Stereo-Based Pedestrian Detection for Collision-Avoidance Applications

Cited by 104 publications

References 23 publications

Real Time Stereo Vision System for Safe Driving Distance Measurement-a Survey

Real Time Stereo Vision System for Safe Driving Distance Measurement-a Survey

The Benefits of Dense Stereo for Pedestrian Detection

A new benchmark for stereo-based pedestrian detection

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