Walking pedestrian recognition

Curio, C; Edelbrunner, Johann; Kalinke, Thomas; Tzomakas, Christos; Seelen, Werner von

doi:10.1109/6979.892152

Cited by 121 publications

(34 citation statements)

References 15 publications

(13 reference statements)

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“…The answer involves two issues. First, good features should (Curio et al, 2000). The one with (red) boundary represents the best fit using a twin-pendulum model.…”

Section: Extraction Of Motion Patternmentioning

confidence: 99%

“…The figure displays a complete cycle of a pedestrian's legs. We develop a computationally efficient human motion analysis algorithm based on the twin-pendulum model introduced in Aggarwal and Cai (1999) and Curio et al (2000). The twin-pendulum model has a very simple form that captures the inherent nature of gait.…”

Section: Cyclic Motionmentioning

confidence: 99%

“…One cue (shape or contour) is used for initial detection and others (motion, gait) are used as verification. For instance, Curio et al (2000) proposed a method for the detection, tracking, and final recognition of pedestrians crossing a moving observer's path. The initial detection process is based on texture analysis and geometric features.…”

Section: Related Workmentioning

confidence: 99%

“…The classification is obtained by a temporal analysis of the walking process. However their algorithm "is restricted to the detection of pedestrians that cross the road" (Curio et al, 2000) and hence is not general enough for robot's situation awareness such as intrusion detection.…”

Section: Related Workmentioning

confidence: 99%

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Pedestrian Detection via Periodic Motion Analysis

Yang

Weiss

Zheng

et al. 2006

Int J Comput Vision

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We describe algorithms for detecting pedestrians in videos acquired by infrared (and color) sensors. Two approaches are proposed based on gait. The first employs computationally efficient periodicity measurements. Unlike other methods, it estimates a periodic motion frequency using two cascading hypothesis testing steps to filter out non-cyclic pixels so that it works well for both radial and lateral walking directions. The extraction of the period is efficient and robust with respect to sensor noise and cluttered background. In order to integrate shape and motion, we convert the cyclic pattern into a binary sequence by Maximal Principal Gait Angle (MPGA) fitting in the second method. It does not require alignment and continuously estimates the period using a Phase-locked Loop. Both methods are evaluated by experimental results that measure performance as a function of size, movement direction, frame rate and sequence length.

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“…The answer involves two issues. First, good features should (Curio et al, 2000). The one with (red) boundary represents the best fit using a twin-pendulum model.…”

Section: Extraction Of Motion Patternmentioning

confidence: 99%

Section: Cyclic Motionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Pedestrian Detection via Periodic Motion Analysis

Yang

Weiss

Zheng

et al. 2006

Int J Comput Vision

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“…For camera-based algorithms, various studies about general human detection [5], pedestrian detections [6,7,8], and human pose recognitions [9,10,20] all emphasize on the importance of human limb motions and corresponding posture changes, which provide strong clues in achieving better image-processing results. Some studies about pedestrian crash test surrogates designs [11,12] also pointed out the changes of human body Radar Cross Section (RCS) with limb motions, and suggested introducing moving pedestrian crash dummies to simulate such RCS variations.…”

Section: Introductionmentioning

confidence: 99%

Pedestrian/Bicyclist Limb Motion Analysis from 110-Car TASI Video Data for Autonomous Emergency Braking Testing Surrogate Development

Sherony¹,

Tian

Chien

et al. 2016

SAE Int. J. Trans. Safety

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Many vehicles are currently equipped with active safety systems that can detect vulnerable road users like pedestrians and bicyclists, to mitigate associated conflicts with vehicles. With the advancements in technologies and algorithms, detailed motions of these targets, especially the limb motions, are being considered for improving the efficiency and reliability of object detection. Thus, it becomes important to understand these limb motions to support the design and evaluation of many vehicular safety systems. However in current literature, there is no agreement being reached on whether or not and how often these limbs move, especially at the most critical moments for potential crashes. In this study, a total of 832 pedestrian walking or cyclist biking cases were randomly selected from one large-scale naturalistic driving database containing 480,000 video segments with a total size of 94TB, and then the 832 video clips were analyzed focusing on their limb motions. We modeled the pedestrian/bicyclist limb motions in four layers: (1) the percentages of pedestrians and bicyclists who have limb motions when crossing the road; (2) the averaged action frequency and the corresponding distributions on when there are limb motions; (3) comparisons of the limb motion behavior between crossing and non-crossing cases; and (4) the effects of seasons on the limb motions when the pedestrians/bicyclists are crossing the road. The results of this study can provide empirical foundations supporting surrogate development, benefit analysis, and standardized testing of vehicular pedestrian/ bicyclist detection and crash mitigation systems.

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Tracking pedestrians using multiple single‐row laser range scanners and its reliability evaluation

Nakamura¹,

Zhao²,

Shibasaki³

et al. 2006

Systems & Computers in Japan

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SUMMARYIn this paper the authors propose a method for pedestrian tracking using multiple laser range scanners and describe verification experiments in a railway station. The proposed method involves synchronizing multiple laser scanners using a network and tracking pedestrians based on range data for foot cross-sections. The tracking algorithm consists of the following functions: detection of foot candidates using range data clustering; detection of pedestrian candidates using grouping of foot candidates; detection of movement vectors for pedestrian candidates; extended processing of existing tracings using an extended Kalman filter based on a walking model. The results of using the proposed method in a station concourse in Tokyo Station found that at a maximum 150 people could be tracked at the same time. The tracking precision exceeded 80% during commuter rush hour. Applications to high-precision crowd observations in wide areas can be expected.

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Walking pedestrian recognition

Cited by 121 publications

References 15 publications

Pedestrian Detection via Periodic Motion Analysis

Pedestrian Detection via Periodic Motion Analysis

Pedestrian/Bicyclist Limb Motion Analysis from 110-Car TASI Video Data for Autonomous Emergency Braking Testing Surrogate Development

Tracking pedestrians using multiple single‐row laser range scanners and its reliability evaluation

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