A survey of video datasets for human action and activity recognition

Chaquet, José M.; Carmona, Enrique J.; Fernández-Caballero, Antonio

doi:10.1016/j.cviu.2013.01.013

Cited by 382 publications

(184 citation statements)

References 142 publications

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“…There is a large volume of literature on capturing spatiotemporal dependencies among visual cues for activity recognition [1,25,5]. Representative models include Dynamic Bayesian Networks [28,27], Hidden Conditional Random Fields (HCRFs) [24,17], hierarchical graphical models [17,16,15,22], AND-OR graphs [21,2], and Logic Networks [19,4].…”

Section: Related Workmentioning

confidence: 99%

HiRF: Hierarchical Random Field for Collective Activity Recognition in Videos

Amer

Peng

Todorovic

2014

Computer Vision – ECCV 2014

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Abstract. This paper addresses the problem of recognizing and localizing coherent activities of a group of people, called collective activities, in video. Related work has argued the benefits of capturing long-range and higher-order dependencies among video features for robust recognition. To this end, we formulate a new deep model, called Hierarchical Random Field (HiRF). HiRF models only hierarchical dependencies between model variables. This effectively amounts to modeling higher-order temporal dependencies of video features. We specify an efficient inference of HiRF that iterates in each step linear programming for estimating latent variables. Learning of HiRF parameters is specified within the max-margin framework. Our evaluation on the benchmark New Collective Activity and Collective Activity datasets, demonstrates that HiRF yields superior recognition and localization as compared to the state of the art.

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Section: Related Workmentioning

confidence: 99%

HiRF: Hierarchical Random Field for Collective Activity Recognition in Videos

Amer

Peng

Todorovic

2014

Computer Vision – ECCV 2014

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“…but these systems have a pervasiveness issue: the only place where the activity is recognized is in the user's home or where the sensors are located. Another kind of research venue focuses on the usage of cameras for the recognition of gestures [20][21][22]. This is especially suitable for security (e.g.…”

Section: Activity Recognition Systems For Eldersmentioning

confidence: 99%

Mobile activity recognition and fall detection system for elderly people using Ameva algorithm

Concepción

Morillo

Álvarez-García

et al. 2017

Pervasive and Mobile Computing

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a b s t r a c tCurrently, the lifestyle of elderly people is regularly monitored in order to establish guidelines for rehabilitation processes or ensure the welfare of this segment of the population. In this sense, activity recognition is essential to detect an objective set of behaviors throughout the day. This paper describes an accurate, comfortable and efficient system, which monitors the physical activity carried out by the user. An extension to an awarded activity recognition system that participated in the EvAAL 2012 and EvAAL 2013 competitions is presented. This approach uses data retrieved from accelerometer sensors to generate discrete variables and it is tested in a non-controlled environment. In order to achieve the goal, the core of the algorithm Ameva is used to develop an innovative selection, discretization and classification technique for activity recognition. Moreover, with the purpose of reducing the cost and increasing user acceptance and usability, the entire system uses only a smartphone to recover all the information required.

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“…Therefore, other datasets were created such as the CAVIAR, ETISEO, CASIA Action, MSR Action, HOLLYWOOD, UCF datasets, Olympic Sports and HMDB51, BEHAVE, TV Human Interaction, UT-Tower, UT-Interaction, etc. Please refer to [37] for a complete list of the currently available datasets in HMA. Due to the advancement of the technology, using networks of multiple cameras for monitoring public places such as airports, shopping malls, etc.…”

Section: [35] L Chen H Wei and J Ferrymanmentioning

confidence: 99%

Fuzzy human motion analysis: A review

Lim

Vats

Chan

2015

Pattern Recognition

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Human Motion Analysis (HMA) is currently one of the most popularly active research domains as such significant research interests are motivated by a number of real world applications such as video surveillance, sports analysis, healthcare monitoring and so on. However, most of these real world applications face high levels of uncertainties that can affect the operations of such applications. Hence, the fuzzy set theory has been applied and showed great success in the recent past. In this paper, we aim at reviewing the fuzzy set oriented approaches for HMA, individuating how the fuzzy set may improve the HMA, envisaging and delineating the future perspectives. To the best of our knowledge, there is not found a single survey in the current literature that has discussed and reviewed fuzzy approaches towards the HMA. For ease of understanding, we conceptually classify the human motion into three broad levels: Low-Level (LoL), Mid-Level (MiL), and High-Level (HiL) HMA.

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A survey of video datasets for human action and activity recognition

Cited by 382 publications

References 142 publications

HiRF: Hierarchical Random Field for Collective Activity Recognition in Videos

HiRF: Hierarchical Random Field for Collective Activity Recognition in Videos

Mobile activity recognition and fall detection system for elderly people using Ameva algorithm

Fuzzy human motion analysis: A review

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