Frame difference history image for gait recognition

Lee, Chun-Chieh; Chuang, Chi-Hung; Hsieh, Jun-Wei; Wu, Ming-Xuan; Fan, Kuo-Chin

doi:10.1109/icmlc.2011.6017007

Cited by 13 publications

(7 citation statements)

References 12 publications

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“…In this line, vision-based gait recognition has been undertaken in the literature by using motion history images and frame difference [12] in an attempt to analyze human motion. Nonetheless, there is a yawning gap between gait recognition and pedestrians' intention or action classification.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Pedestrian path prediction using body language traits

Quintero

Almeida

Llorca

et al. 2014

2014 IEEE Intelligent Vehicles Symposium Proceedings

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Driver Assistance Systems have achieved a high level of maturity in the latest years. As an example of that, sophisticated pedestrian protection systems are already available in a number of commercial vehicles from several OEMs. However, accurate pedestrian path prediction is needed in order to go a step further in terms of safety and reliability, since it can make the difference between effective and non-effective intervention. In this paper, we consider the three-dimensional pedestrian body language in order to perform path prediction in a probabilistic framework. For this purpose, the different body parts and joints are detected using stereo vision. We propose the use of GPDM (Gaussian Process Dynamical Models) for reducing the high dimensionality of the input feature vector (composed by joints and displacement vectors) in the 3D pose space and for learning the pedestrian dynamics in a latent space. Experimental results show that accurate path prediction can be achieved at a time horizon of ≈ 0.8 s.

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Section: Introduction and Related Workmentioning

confidence: 99%

Pedestrian path prediction using body language traits

Quintero

Almeida

Llorca

et al. 2014

2014 IEEE Intelligent Vehicles Symposium Proceedings

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“…by [26], [27]. Especially, for capture and classification of gait, motion history images (MHIs) [28] of stride [29], gait [30] and frame difference [31] have been proposed that emphasize the weight of successive contours in gait recognition. For a comprehensive review of these works, see [32].…”

Section: Related Workmentioning

confidence: 99%

Early detection of the Pedestrian's intention to cross the street

Köhler

Goldhammer

Bauer

et al. 2012

2012 15th International IEEE Conference on Intelligent Transportation Systems

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Abstract-This paper focuses on monocular-video-based stationary detection of the pedestrian's intention to enter the traffic lane. We propose a motion contour image based HOG-like descriptor, MCHOG, and a machine learning algorithm that reaches the decision at an accuracy of 99 % within the initial step at the curb of smart infrastructure. MCHOG implicitly comprises the body language of gait initiation, especially the body bending and the spread of legs. In a case study at laboratory conditions we present ROC performance data and an evaluation of the span of time necessary for recognition. While MCHOG in special cases indicates detection of the intention before the whole body moves, on average it allows for detection of the movement within 6 frames at a frame rate of 50 Hz and an accuracy of 80 %. Feasibility of the method in a real world intersection scenario is demonstrated.

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“…legs and arms, which are less affected by changes in carrying conditions, clothing, and surface. Various other silhouette-based gait representations have been also developed, including Average Energy Image (AEI) [11], Frame Difference History Image (FDHI) [12] and an optical flow based representation [13]. Most of the recently proposed gait representations are designed to be insensitive against certain covariate condition changes.…”

Section: Gait Representationsmentioning

confidence: 99%

Uncooperative gait recognition by learning to rank

Martín-Félez

Xiang

2014

Pattern Recognition

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Frame difference history image for gait recognition

Cited by 13 publications

References 12 publications

Pedestrian path prediction using body language traits

Pedestrian path prediction using body language traits

Early detection of the Pedestrian's intention to cross the street

Uncooperative gait recognition by learning to rank

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