Fuzzy human motion analysis: A review

Lim, Chun Shen; Vats, Ekta; Chan, Chee Seng

doi:10.1016/j.patcog.2014.11.016

Cited by 79 publications

(21 citation statements)

References 234 publications

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“…In their survey, it was shown that methods exploiting semantic information yield superior performance compared to their nonsemantic counterparts in many scenarios. Human action recognition in still images is also discussed within the context of fuzzy domain in a recent survey [34].…”

Section: Related Workmentioning

confidence: 99%

Scale coding bag of deep features for human attribute and action recognition

Khan

Weijer

Anwer

et al. 2017

Machine Vision and Applications

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Most approaches to human attribute and action recognition in still images are based on image representation in which multi-scale local features are pooled across scale into a single, scale-invariant encoding. Both in bagof-words and the recently popular representations based on convolutional neural networks, local features are computed at multiple scales. However, these multi-scale convolutional features are pooled into a single scale-invariant representation. We argue that entirely scale-invariant image representations are sub-optimal and investigate approaches to scale coding within a bag of deep features framework. Our approach encodes multi-scale information explicitly during the image encoding stage. We propose two strategies to encode multi-scale information explicitly in the final image representation. We validate our two scale coding techniques on five datasets: Willow, PASCAL VOC 2010, PASCAL VOC 2012, Stanford-40 and Human Attributes (HAT-27). On all datasets, the proposed scale coding approaches outperform both the scale-invariant method and the standard deep features of the same network. Further, combining our scale coding approaches with standard deep features leads to consistent improvement over the state of the art.

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Section: Related Workmentioning

confidence: 99%

Scale coding bag of deep features for human attribute and action recognition

Khan

Weijer

Anwer

et al. 2017

Machine Vision and Applications

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“…Anger has the emotion recognized with highest accuracy. [9] and multimodal fusion [3,4]. Castellano et al [7] extended their work to multimodal framework integrating face-expression, body-gestures, and speech.…”

Section: Implementing Emotional Phrase Matchingmentioning

confidence: 99%

Towards Formal Multimodal Analysis of Emotions for Affective Computing

Ghayoumi

Bansal

Thafar

2016

International Conferences on Distributed Multimedia Systems

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Abstract-Social robotics is related to the robotic systems and human interaction. Social robots have applications in elderly care, health care, home care, customer service and reception in industrial settings. Human-Robot Interaction (HRI) requires better understanding of human emotion.There are few multimodal fusion systems that integrate limited amount of facial expression, speech and gesture analysis. In this paper, we describe the implementation of a semantic algebra based formal model that integrates six basic facial expressions, speech phrases and gesture trajectories. The system is capable of real-time interaction. We used the decision level fusion approach for integration and the prototype system has been implemented using Matlab.

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“…Walking, running, lifting, and jumping are the most common human movements in daily life [1]. Consequently, controlling, understanding and recognizing human movements are important tasks for people in the fields of robotics, computer vision, ergonomics, and biomechanics [2][3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

An Improved Nonstationary Fuzzy System Approach versus Type-2 Fuzzy System for the Lifting Motion Control with Human-in-the-Loop Simulation

Karaköse¹

2018

IJCIS

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People working in the fields of robotics, animation, computer graphics and computer vision, and biomechanics, find it difficult to conduct human motion simulations, such as lifting, walking and running. This is because it is difficult to predict all of the motion strategies in a variety of situations. The human lifting motion is hard work; at the same time, lifting is the most demanded robotic motion. In this paper, a fuzzy integral based nonstationary fuzzy inference system is proposed to control a five-segment human model for the human lifting motion with human-inthe-loop simulation. This system was designed to reduce the computational complexity of nonstationary fuzzy systems. Hence, this paper contributes to the literature in two ways: as an improved nonstationary fuzzy system and as a human lifting motion simulation framework. A fuzzy integral algorithm between the fuzzification and defuzzification stages has been used to aggregate the inference outputs of the nonstationary fuzzy controller. The fuzzy integral algorithm uses fuzzy values obtained during the fuzzification stage as the attribute values and the fuzzy values obtained by a one-loop quantum particle swarm optimization algorithm as the importance values. The computational complexity in the nonstationary fuzzy systems and type-2 fuzzy systems can be reduced between 25 to 60 percent with the improved nonstationary fuzzy system. An experimental application of the human lifting motion was carried out to demonstrate the effectiveness of the proposed approach. The results illustrated that the proposed algorithm can achieve increased simplicity, improved effectiveness, good robustness, and a higher precision of computation.

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Fuzzy human motion analysis: A review

Cited by 79 publications

References 234 publications

Scale coding bag of deep features for human attribute and action recognition

Scale coding bag of deep features for human attribute and action recognition

Towards Formal Multimodal Analysis of Emotions for Affective Computing

An Improved Nonstationary Fuzzy System Approach versus Type-2 Fuzzy System for the Lifting Motion Control with Human-in-the-Loop Simulation

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