Learning shape variations of motion trajectories for gait analysis

Devanne, Maxime; Wannous, Hazem; Daoudi, Mohamed; Bimbo, Alberto Del; Pala, Pietro

doi:10.1109/icpr.2016.7899749

Cited by 17 publications

(11 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We also performed our approached by using depth and skeleton information on this dataset and details will be shown in the following sections. There are several works [62] and a comprehensive survey [63] of existing space-time representations of people based on 3D skeletal data.…”

mentioning

confidence: 99%

Distances evolution analysis for online and off-line human object interaction recognition

Meng

Drira

Boonært

2018

Image and Vision Computing

View full text Add to dashboard Cite

Human action recognition in 3D sequences is one of the most challenging and active areas of research in the computer vision domain. However designing automatic systems that are robust to significant variability due to object combinations and high complexity of human motions are more challenging in addition to the typical requirements such as rotation, translation, and scale invariance is challenging task. In this paper, we propose a spatio-temporal modeling of human-object interaction videos for on-line and off-line recognition. The inter joint distances and the object are considered as low-level features for online classification. For off-line recognition, we propose rate-invariant classification of full video and early recognition. A shape analysis of trajectories of the inter-joint and object-joints distances is proposed for this end. The experiments conducted following state-of-the-art settings using MSR Daily Activity 3D Dataset and Online RGBD Action Dataset and on a new Multi-view dataset for human object interaction demonstrate that the proposed approach is effective and discriminative for human object interaction classification as demonstrated here.

show abstract

mentioning

confidence: 99%

Distances evolution analysis for online and off-line human object interaction recognition

Meng

Drira

Boonært

2018

Image and Vision Computing

View full text Add to dashboard Cite

show abstract

“…As already mentioned, the pioneering works on human motion analysis and gait recognition fall into the category of marker based techniques [8,22]. Since then, various methods [76] and modalities [10,26,77] were proposed to determine one's identity. Usu-ally, the vision-based methods start with extracting the human silhouette in order to obtain the spatiotemporal data describing the walking person.…”

Section: Background and Relevant Workmentioning

confidence: 99%

Calibrated and synchronized multi-view video and motion capture dataset for evaluation of gait recognition

Kwolek

Michalczuk

Krzeszowski

et al. 2019

Multimed Tools Appl

View full text Add to dashboard Cite

We introduce synchronized and calibrated multi-view video and motion capture dataset for motion analysis and gait identification. The 3D gait dataset consists of 166 data sequences with 32 people. In 128 data sequences, each of 32 individuals was dressed in his/her clothes, in 24 data sequences, 6 of 32 performers changed clothes, and in 14 data sequences, 7 of the performers had a backpack on his/her back. In a single recording session, every performer walked from right to left, then from left to right, and afterwards on the diagonal from upperright to bottom-left and from bottom-left to upper-right corner of a rectangular scene. We demonstrate that a baseline algorithm achieves promising results in a challenging scenario, in which gallery/training data were collected in walks perpendicular/facing to the cameras, whereas the probe/testing data were collected in diagonal walks. We compare performances of biometric gait recognition that were achieved on marker-less and marker-based 3D data. We present recognition performances, which were achieved by a convolutional neural network and classic classifiers operating on gait signatures obtained by multilinear principal component analysis. The availability of synchronized multi-view image sequences with 3D locations of body markers creates a number of possibilities for extraction of discriminative gait signatures. The gait data are available at http://bytom.pja.edu.pl/projekty/hm-gpjatk/.

show abstract

“…As an alternative to the fixed-window segmentation scheme, we divide the continuous sequence into motion units by automatically detecting salient motion changes. We consider two existing approaches based on Principal Component Analysis [2] and standard deviation within an sliding window [10]. While these approaches are efficient for simple movements like actions, their efficiency on more complex movements like daily activities is not straightforward.…”

Section: Fig 3: Illustration Of the Fixed-length Window Segmentationmentioning

confidence: 99%

Recognition of Activities of Daily Living via Hierarchical Long-Short Term Memory Networks

Devanne

Papadakis

Nguyen

2019

2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)

Self Cite

View full text Add to dashboard Cite

In order to offer optimal and personalized assistance services to frail people, smart homes or assistive robots must be able to understand the context and activities of users. With this outlook, we propose a vision-based approach for understanding activities of daily living (ADL) through skeleton data captured using an RGB-D camera. Upon decomposition of a skeleton sequence into short temporal segments, activities are classified via a hierarchical two-layer Long-Short Term Memory Network (LSTM) allowing to analyse the sequence at different levels of temporal granularity. The proposed approach is evaluated on a very challenging daily activity dataset wherein we attain superior performance. Our main contribution is a multi-scale, temporal dependency model of activities, founded on a comparison of context features that characterize previous recognition results and a hierarchical representation with a low-level behaviour-unit recognition layer and a high-level units chaining layer.

show abstract

Learning shape variations of motion trajectories for gait analysis

Cited by 17 publications

References 29 publications

Distances evolution analysis for online and off-line human object interaction recognition

Distances evolution analysis for online and off-line human object interaction recognition

Calibrated and synchronized multi-view video and motion capture dataset for evaluation of gait recognition

Recognition of Activities of Daily Living via Hierarchical Long-Short Term Memory Networks

Contact Info

Product

Resources

About