Classifying Behavioral Attributes Using Conditional Random Fields

Vrigkas, Michalis; Nikou, Christophoros; Kakadiadis, Ioannis A.

doi:10.1007/978-3-319-07064-3_8

Cited by 11 publications

(12 citation statements)

References 11 publications

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“…Social networking CCV (Jiang et al, 2011) 2001 20 9.317 FPSI (Fathi et al, 2012) 2012 6 8 ≈42 h 1,280 × 720 Broadcast field hockey (Lan et al, 2012b) 2012 11 58 USAA (Fu et al, 2012) 2012 8 ≈200 Sports-1M (Karpathy et al, 2014) 2014 487 1 M ActivityNet (Heilbron et al, 2015) 2015 203 27,801 1,280 × 720 WWW Crowd (Shao et al, 2015) 2015 94 10,000 640 × 360 Behavior BEHAVE (Fisher, 2007a) 2007 8 321 640 × 480 Canal9 (Vinciarelli et al, 2009) 2009 2 190 ≈42 h 720 × 576 USC Creative IT (Metallinou et al, 2010) 2010 50 16 100 Parliament (Vrigkas et al, 2014b) 2014 3 20 228 320 × 240 a frontal view camera. The non-complex backgrounds and the non-intraclass variations in human movements make these datasets non-applicable for real world applications.…”

Section: Discussionmentioning

confidence: 99%

“…(Liu et al, 2011b;Martinez et al, 2014). Behavioral methods aim to recognize behavioral attributes, non-verbal multimodal cues, such as gestures, facial expressions, and auditory cues (Song et al, 2012a;Vrigkas et al, 2014b). Finally, social networking methods model the characteristics and the behavior of humans in several layers of human-to-human interactions in social events from gestures, body motion, and speech (Patron-Perez et al, 2012;Marín-Jiménez et al, 2014).…”

Section: Previous Surveys and Taxonomiesmentioning

confidence: 99%

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A Review of Human Activity Recognition Methods

2015

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Recognizing human activities from video sequences or still images is a challenging task due to problems, such as background clutter, partial occlusion, changes in scale, viewpoint, lighting, and appearance. Many applications, including video surveillance systems, human-computer interaction, and robotics for human behavior characterization, require a multiple activity recognition system. In this work, we provide a detailed review of recent and state-of-the-art research advances in the field of human activity classification. We propose a categorization of human activity methodologies and discuss their advantages and limitations. In particular, we divide human activity classification methods into two large categories according to whether they use data from different modalities or not. Then, each of these categories is further analyzed into sub-categories, which reflect how they model human activities and what type of activities they are interested in. Moreover, we provide a comprehensive analysis of the existing, publicly available human activity classification datasets and examine the requirements for an ideal human activity recognition dataset. Finally, we report the characteristics of future research directions and present some open issues on human activity recognition.

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Section: Discussionmentioning

confidence: 99%

Section: Previous Surveys and Taxonomiesmentioning

confidence: 99%

A Review of Human Activity Recognition Methods

2015

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“…Indeed, it is evident that the difference in appearance between various activities is more noticeable than among people performing the same activity. So, techniques based on the appearance of body parts are proposed as in [203]. (d) 3D models: They attempt to build matches between characteristics of the model based on various features of the images.…”

Section: I) First Stage Methods (Detection)mentioning

confidence: 99%

Vision-based human activity recognition: a survey

Beddiar

Nini

Sabokrou

et al. 2020

Multimed Tools Appl

330

117

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Human activity recognition (HAR) systems attempt to automatically identify and analyze human activities using acquired information from various types of sensors. Although several extensive review papers have already been published in the general HAR topics, the growing technologies in the field as well as the multi-disciplinary nature of HAR prompt the need for constant updates in the field. In this respect, this paper attempts to review and summarize the progress of HAR systems from the computer vision perspective. Indeed, most computer vision applications such as human computer interaction, virtual reality, security, video surveillance and home monitoring are highly correlated to HAR tasks. This establishes new trend and milestone in the development cycle of HAR systems. Therefore, the current survey aims to provide the reader with an up to date analysis of vision-based HAR related literature and recent progress in the field. At the same time, it will highlight the main challenges and future directions.

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“…Parliament [55]: This dataset is a collection of 228 video sequences, depicting political speeches in the Greek parliament, at a resolution of 320 × 240 pixels at 25 fps. The video sequences were manually labeled with one of three behavioral labels: friendly, aggressive, or neutral.…”

Section: Datasetsmentioning

confidence: 99%

Inferring Human Activities Using Robust Privileged Probabilistic Learning

Vrigkas

Kazakos

Nikou

et al. 2017

2017 IEEE International Conference on Computer Vision Workshops (ICCVW)

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In this work, a novel method based on the learning using privileged information (LUPI) paradigm for recognizing complex human activities is proposed that handles missing information during testing. We present a supervised probabilistic approach that integrates LUPI into a hidden conditional random field (HCRF) model. The proposed model is called HCRF+ and may be trained using both maximum likelihood and maximum margin approaches. It employs a self-training technique for automatic estimation of the regularization parameters of the objective functions. Moreover, the method provides robustness to outliers (such as noise or missing data) by modeling the conditional distribution of the privileged information by a Student's t-density function, which is naturally integrated into the HCRF+ framework. Different forms of privileged information were investigated. The proposed method was evaluated using four challenging publicly available datasets and the experimental results demonstrate its effectiveness with respect to the-state-of-the-art in the LUPI framework using both hand-crafted features and features extracted from a convolutional neural network.Index Terms-Hidden conditional random fields, learning using privileged information, human activity recognition ✦

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Classifying Behavioral Attributes Using Conditional Random Fields

Cited by 11 publications

References 11 publications

A Review of Human Activity Recognition Methods

A Review of Human Activity Recognition Methods

Vision-based human activity recognition: a survey

Inferring Human Activities Using Robust Privileged Probabilistic Learning

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