Stereo-based preprocessing for human shape localization in unstructured environments

Broggi, Alberto; Fascioli, Alessandra; Fedriga, I.; Tibaldi, Alessandro; Rose, Michael Del

doi:10.1109/ivs.2003.1212946

Cited by 33 publications

(28 citation statements)

References 8 publications

(12 reference statements)

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“…Zhao and Thorpe (2000) obtain a foreground region by clustering in the disparity space. Broggi et al (2003) and Grubb et al (2004) consider the x-and y-projections of the disparity space following the 'V-disparity' technique (Labayrade et al, 2002).…”

Section: Previous Workmentioning

confidence: 99%

“…Approaches that are overly "bottomup", such as those clustering sparse depth (e.g. Zhao and Thorpe, 2000) or those based on detecting obstacles using the V-disparity technique (Broggi et al, 2003;Grubb et al, 2004), indeed tend to break down in such cluttered scenarios, when pedestrians are not well separated from other objects in the environment and/or when road surface is significantly occluded by other obstacles. In such cases, pedestrian features are frequently merged with other objects in the environment and a subsequent pattern classifier has difficulties dealing with the resulting spatial misalignment.…”

Section: The Protector Systemmentioning

confidence: 99%

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Multi-cue Pedestrian Detection and Tracking from a Moving Vehicle

Gavrila

Munder

2006

Int J Comput Vision

467

328

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Abstract. This paper presents a multi-cue vision system for the real-time detection and tracking of pedestrians from a moving vehicle. The detection component involves a cascade of modules, each utilizing complementary visual criteria to successively narrow down the image search space, balancing robustness and efficiency considerations. Novel is the tight integration of the consecutive modules: (sparse) stereo-based ROI generation, shape-based detection, texture-based classification and (dense) stereo-based verification. For example, shape-based detection activates a weighted combination of texture-based classifiers, each attuned to a particular body pose.Performance of individual modules and their interaction is analyzed by means of Receiver Operator Characteristics (ROCs). A sequential optimization technique allows the successive combination of individual ROCs, providing optimized system parameter settings in a systematic fashion, avoiding ad-hoc parameter tuning. Application-dependent processing constraints can be incorporated in the optimization procedure.Results from extensive field tests in difficult urban traffic conditions suggest system performance is at the leading edge.

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Section: Previous Workmentioning

confidence: 99%

Section: The Protector Systemmentioning

confidence: 99%

Multi-cue Pedestrian Detection and Tracking from a Moving Vehicle

Gavrila

Munder

2006

Int J Comput Vision

467

328

View full text Add to dashboard Cite

show abstract

“…In [50], a foreground region is obtained by clustering in disparity space. In [3] and [18], it is proposed that ROIs be selected by considering the x-and y-projections of the disparity space following the v-disparity representation [24]. In [1], object hypotheses are obtained by using a subtractive clustering in the 3-D space in world coordinates.…”

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 preprocessing for human shape localization in unstructured environments

Cited by 33 publications

References 8 publications

Multi-cue Pedestrian Detection and Tracking from a Moving Vehicle

Multi-cue Pedestrian Detection and Tracking from a Moving Vehicle

The Benefits of Dense Stereo for Pedestrian Detection

A new benchmark for stereo-based pedestrian detection

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