Pedestrian Detection Using Stereo and Biometric Information

Kelly, Philip; Cooke, Eddie; O’Connor, Noel E.; Smeaton, Alan F.

doi:10.1007/11867661_72

Cited by 11 publications

(37 citation statements)

References 9 publications

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“…This paper extends the work described in [8] with two important contributions. The first is the enhancement of the dense disparity algorithm which is the basis for obtaining 3D information from a given scene and without which the accuracy of the pedestrian detection technique deteriorates significantly.…”

Section: Introductionsupporting

confidence: 68%

“…In this paper we present a significant improvement of our existing pedestrian detection technique descried in [8]. This technique can be applied using a stereo camera system that is located, as most surveillance cameras are, above human height and orientated at approximately a 45 degree angle to look down on pedestrians below, making this technique applicable to both indoor and outdoor scenarios.…”

Section: Introductionmentioning

confidence: 99%

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Pedestrian detection in uncontrolled environments using stereo and biometric information

Kelly

O’Connor

Smeaton

2006

Proceedings of the 4th ACM International Workshop on Video Surveillance and Sensor Networks

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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.

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Section: Introductionsupporting

confidence: 68%

Section: Introductionmentioning

confidence: 99%

Pedestrian detection in uncontrolled environments using stereo and biometric information

Kelly

O’Connor

Smeaton

2006

Proceedings of the 4th ACM International Workshop on Video Surveillance and Sensor Networks

Self Cite

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“…From this previous inequality it can be seen that the maximum distance permitted between two merging regions is constrained by the parameter δ. The value of δ is obtained by a biometric pedestrian model [25] that is based on the Golden Ratio, Φ = √ 5 * 0.5 + 0.5 1.618. This parameter Φ can be used to define the approximate proportions of a human body if the height of the person is known [27].…”

Section: Pedestrian Detectionmentioning

confidence: 99%

“…Finally, all remaining disparity points are retained and labelled as foreground 3D points. For further information on the post-processing of disparity values readers are directed to [25].…”

Section: Introductionmentioning

confidence: 99%

Robust pedestrian detection and tracking in crowded scenes

Kelly

O’Connor

Smeaton

2009

Image and Vision Computing

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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.

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“…Other methods are based on some type of additional information like IR images [4], or depth information [5], [4]. Most of pedestrian detection methods that use depth information rely on the disparity map and make some kind of segmentation on this map to detect objects [5], or use a v-disparity approach [4].…”

Section: A Related Workmentioning

confidence: 99%

Multi-feature walking pedestrians detection for driving assistance systems

Bota¹,

Nedesvchi²

2008

IET Intell. Transp. Syst.

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Abstract-This paper deals with a pedestrian pre-crash sensor based on dense stereovision and motion analysis. We aim to provide means to deploy safety measures before a crash occurs. Depending on the concrete selected actuator, it could be interesting to know the type of object (e. g. vehicle, pedestrian, bicyclist, etc.) involved in the collision. Some actuators need the information about the collision partner type, because they have to be triggered only in specific situations, or in a situation dependent manner. We present a pedestrian detection system based on dense stereo and motion, acting as pre-crash sensor. In order to detect pedestrians, we use simple features such as object size, speed, and also more complex, motion based features. All these features are combined into a flexible, Bayesian framework.

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Pedestrian Detection Using Stereo and Biometric Information

Cited by 11 publications

References 9 publications

Pedestrian detection in uncontrolled environments using stereo and biometric information

Pedestrian detection in uncontrolled environments using stereo and biometric information

Robust pedestrian detection and tracking in crowded scenes

Multi-feature walking pedestrians detection for driving assistance systems

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