Human Pose Estimation and Activity Recognition From Multi-View Videos: Comparative Explorations of Recent Developments

Holte, Michael Boelstoft; Tran, Cuong; Trivedi, Mohan M.; Moeslund, Thomas B.

doi:10.1109/jstsp.2012.2196975

Cited by 140 publications

(68 citation statements)

References 76 publications

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“…where x avg = m 10 m 00 and y avg = m 01 m 00 (5) By applying normalization, scale-invariant moments are obtained. Hence, normalized central moments are defined as follows [35].…”

Section: Hu-moments Invariant Featuresmentioning

confidence: 99%

Human Action Recognition from Multiple Views Based on View-Invariant Feature Descriptor Using Support Vector Machines

2016

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This paper presents a novel feature descriptor for multiview human action recognition. This descriptor employs the region-based features extracted from the human silhouette. To achieve this, the human silhouette is divided into regions in a radial fashion with the interval of a certain degree, and then region-based geometrical and Hu-moments features are obtained from each radial bin to articulate the feature descriptor. A multiclass support vector machine classifier is used for action classification. The proposed approach is quite simple and achieves state-of-the-art results without compromising the efficiency of the recognition process. Our contribution is two-fold. Firstly, our approach achieves high recognition accuracy with simple silhouette-based representation. Secondly, the average testing time for our approach is 34 frames per second, which is much higher than the existing methods and shows its suitability for real-time applications. The extensive experiments on a well-known multiview IXMAS (INRIA Xmas Motion Acquisition Sequences) dataset confirmed the superior performance of our method as compared to similar state-of-the-art methods.

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“…where x avg = m 10 m 00 and y avg = m 01 m 00 (5) By applying normalization, scale-invariant moments are obtained. Hence, normalized central moments are defined as follows [35].…”

Section: Hu-moments Invariant Featuresmentioning

confidence: 99%

Human Action Recognition from Multiple Views Based on View-Invariant Feature Descriptor Using Support Vector Machines

2016

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“…These approaches are simple and have produced state-of-the-art results on Weizmann, KTH, and multi-view IXMAS datasets as recorded in Table 2. There are two major approaches for multi-view human action recognition base on shape and motion features: 3D approach and 2D approach [26]. As indicated in Table 2, 3D approaches provide higher accuracy than 2D approaches but at higher computational cost which makes these approaches less applicable for real time applications.…”

Section: Discussionmentioning

confidence: 99%

“…These approaches are simple and have produced state-of-the-art results on Weizmann, KTH, and multiview IXMAS datasets as recorded in Table 2. There are two major approaches for multi-view human action recognition base on shape and motion features: 3D approach and 2D approach [26]. As …”

Section: Fuzzy Logic-based Approachesmentioning

confidence: 99%

“…However, these sensors also have several limitations such as these sensors only capture the frontal surfaces of the human and other objects in the scene. In addition to this, these sensors also have limited range about 6-7 m, and data can be distorted by scattered light from the reflective surfaces [26]. However, there is no universal rule for selecting the appropriate camera; it mainly depends on the nature of the problem and its requirements.…”

Section: Introductionmentioning

confidence: 99%

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A Comprehensive Review on Handcrafted and Learning-Based Action Representation Approaches for Human Activity Recognition

2017

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Human activity recognition (HAR) is an important research area in the fields of human perception and computer vision due to its wide range of applications. These applications include: intelligent video surveillance, ambient assisted living, human computer interaction, human-robot interaction, entertainment, and intelligent driving. Recently, with the emergence and successful deployment of deep learning techniques for image classification, researchers have migrated from traditional handcrafting to deep learning techniques for HAR. However, handcrafted representation-based approaches are still widely used due to some bottlenecks such as computational complexity of deep learning techniques for activity recognition. However, approaches based on handcrafted representation are not able to handle complex scenarios due to their limitations and incapability; therefore, resorting to deep learning-based techniques is a natural option. This review paper presents a comprehensive survey of both handcrafted and learning-based action representations, offering comparison, analysis, and discussions on these approaches. In addition to this, the well-known public datasets available for experimentations and important applications of HAR are also presented to provide further insight into the field. This is the first review paper of its kind which presents all these aspects of HAR in a single review article with comprehensive coverage of each part. Finally, the paper is concluded with important discussions and research directions in the domain of HAR.

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“…A natural choice for the design of such a system is the use of multicameras [17], [18]. Multicamera systems exist in many other applications, such as gesture recognition [19], [20], human body pose and activity recognition [21], face detection, tracking and pose estimation in intelligent space, etc. A thorough study of such systems in a vehicular setting utilizing naturalistic driving data, however, is lacking in literature.…”

Section: Continuous Head Movement Estimator Formentioning

confidence: 99%

Continuous Head Movement Estimator for Driver Assistance: Issues, Algorithms, and On-Road Evaluations

Tawari

Martin

Trivedi

2014

IEEE Trans. Intell. Transport. Syst.

104

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Abstract-Analysis of a driver's head behavior is an integral part of a driver monitoring system. In particular, the head pose and dynamics are strong indicators of a driver's focus of attention. Many existing state-of-the-art head dynamic analyzers are, however, limited to single-camera perspectives, which are susceptible to occlusion of facial features from spatially large head movements away from the frontal pose. Nonfrontal glances away from the road ahead, however, are of special interest since interesting events, which are critical to driver safety, occur during those times. In this paper, we present a distributed camera framework for head movement analysis, with emphasis on the ability to robustly and continuously operate even during large head movements. The proposed system tracks facial features and analyzes their geometric configuration to estimate the head pose using a 3-D model. We present two such solutions that additionally exploit the constraints that are present in a driving context and video data to improve tracking accuracy and computation time. Furthermore, we conduct a thorough comparative study with different camera configurations. For experimental evaluations, we collected a novel head pose data set from naturalistic on-road driving in urban streets and freeways, with particular emphasis on events inducing spatially large head movements (e.g., merge and lane change). Our analyses show promising results.

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Human Pose Estimation and Activity Recognition From Multi-View Videos: Comparative Explorations of Recent Developments

Cited by 140 publications

References 76 publications

Human Action Recognition from Multiple Views Based on View-Invariant Feature Descriptor Using Support Vector Machines

Human Action Recognition from Multiple Views Based on View-Invariant Feature Descriptor Using Support Vector Machines

A Comprehensive Review on Handcrafted and Learning-Based Action Representation Approaches for Human Activity Recognition

Continuous Head Movement Estimator for Driver Assistance: Issues, Algorithms, and On-Road Evaluations

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