Multi-view action recognition based on action volumes, fuzzy distances and cluster discriminant analysis

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.

show abstract

“…Nonetheless, they tackled the problem of interaction recognition i.e. human action recognition involving two persons [165].…”

Section: Multi-view Action Recognitionmentioning

confidence: 99%

“…[162,163,164,165] Multi-view posture patterns are generated by utilizing FVQ to build a multi-view fuzzy motion model in order to support view invariant human action recognition.…”

Section: Fuzzy Descriptor Action Modelmentioning

confidence: 99%

Fuzzy human motion analysis: A review

Lim

Vats

Chan

2015

Pattern Recognition

View full text Add to dashboard Cite

show abstract

“…Furthermore, they do not suffer from background subtraction problems [18], as is the case when employing silhouette-based activity recognition methods [2] [19] [20] [21], and there is no need to track particular body parts, e.g., arms or feet [22], for activity recognition.…”

Section: Related Workmentioning

confidence: 99%

Exploiting stereoscopic disparity for augmenting human activity recognition performance

Mademlis

Iosifidis

Tefas

et al. 2015

Multimed Tools Appl

Self Cite

View full text Add to dashboard Cite

General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Full terms of use are available: http://www.bristol.ac.uk/pure/about/ebr-terms Abstract-This work investigates several ways to exploit scene depth information, implicitly available through the modality of stereoscopic disparity in 3D videos, with the purpose of augmenting performance in the problem of recognizing complex human activities in natural settings. The standard state-of-the-art activity recognition algorithmic pipeline consists in the consecutive stages of video description, video representation and video classification. Multimodal, depth-aware modifications to standard methods are being proposed and studied, both for video description and for video representation, that indirectly incorporate scene geometry information derived from stereo disparity. At the description level, this is made possible by suitably manipulating video interest points based on disparity data. At the representation level, the followed approach represents each video by multiple vectors corresponding to different disparity zones, resulting in multiple activity descriptions defined by disparity characteristics. In both cases, a scene segmentation is thus implicitly implemented, based on the distance of each imaged object from the camera during video acquisition. The investigated approaches are flexible and able to cooperate with any monocular low-level feature descriptor. They are evaluated using a publicly available activity recognition dataset of unconstrained stereoscopic 3D videos, consisting in extracts from Hollywood movies, and compared both against competing depth-aware approaches and a state-of-the-art monocular algorithm. Quantitative evaluation reveals that some of the examined approaches achieve state-of-the-art performance.

show abstract

“…An important subgroup of these works provide algorithms for activity recognition from a set of silhouettes of the subject, such as [19] or [11]. In [19] Fourier Transform in cylindrical coordinates is performed to compare motion history volumes representing different actions.…”

Section: Introductionmentioning

confidence: 99%

“…In [19] Fourier Transform in cylindrical coordinates is performed to compare motion history volumes representing different actions. In [11], the so-called action volume is produced from a set of human body silhouettes from the same view angle. They combine multiview angles to obtain a set of representative action volumes that are used to classify the action.…”

Section: Introductionmentioning

confidence: 99%

Designing a Topological Algorithm for 3D Activity Recognition

Jiménez

Medrano

Monaghan

et al. 2016

Computational Topology in Image Context

View full text Add to dashboard Cite

Abstract. Voxel carving is a non-invasive and low-cost technique that is used for the reconstruction of a 3D volume from images captured from a set of cameras placed around the object of interest. In this paper we propose a method to topologically analyze a video sequence of 3D reconstructions representing a tennis player performing different forehand and backhand strokes with the aim of providing an approach that could be useful in other sport activities.

show abstract

Multi-view action recognition based on action volumes, fuzzy distances and cluster discriminant analysis

Cited by 61 publications

References 35 publications

Fuzzy human motion analysis: A review

Fuzzy human motion analysis: A review

Exploiting stereoscopic disparity for augmenting human activity recognition performance

Designing a Topological Algorithm for 3D Activity Recognition

Contact Info

Product

Resources

About