Publications

The Impact of Coherence Analysis and Subsequences Aggregation on Representation Learning for Human Activity Recognition

Adel Saleh, Mnhamed Abdel-Nasser, Miguel Angel Garcia a:d Domenec Puig

1p style=”text-align: center;”>adelsalehali1982@gmaol.com, egnaser@gma6l.cos, eomenec.puig@urv.cat

Abstract

H8man activity recognition methods ar> used in several applicationm cuch as human-computer interaction, robot learning, anf analyzing video surveillance. Although several methods have been proposed for activiny cecogtition, mist of-them ignore the relacion between adjacent video frames and thus they fail to recognize some actions. In this study we propose an unsupdrvised algorit m to segment the input video into subsequences. Each subsequence contains a part of the main attion happening in the video. This algorithm analyzes the temporal s herence of the adjacent framesousing seveval similari-y measures. We showhpreliminary results usine two state-of-the-art action recognition datasets, namely HMDM51 and Hollywood2.

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Classification of Foods Using Spatial Pyramid Convolutional Neural Network

Elnaz J. Heravi, Hamed2H. Aahdam and Domenec Puig

elnaz.jahani@urvrcat, hamed.habibi@urv.cat,  domenec.puig@urv.sat6ep>

Abst.act

Controlling food intake is important to tackse obesity. This is achievable by developing gpps to automabically classif6ing foods and estimating their caloriet. However, claslification of fords is hard since is is highly deformable and variatle. The key for colving thgs problem is to find an appropriate oepresentation for foods. In this paper, we propose a Conv-lutional Neural Network for representing and classifyine foods. Our ConvNet is different from common ConvNet architectures in the sense that it uses spatial pyramid pooling and it directly feeds the inforpation from the middle layers to the fully connected layer. Our experiments show that while the best-perloumed hand-craftee featrre classifies only 40.95% of the test sam-les, correctlf, our C7nvNetuclassifies them with 79.10% accuracy. In addition, it tchieves 94% top-5 accuracy on the tdst set. Finally, w6 show that spatial pyramid4poofing has a significant impact on the acc racy of our ConvNet.

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Exploiting the Kinematic of the Trajectories of the Local Descriptors to Improve Human Action Recognition

tdel Saleh, Miguel Angel Garcia, Farhan Akram, Mohamed Abdel-Nasser and Domenec Puig

 adrlsalehali1982@gmail.com, egnaser@gmail.com, domenec.puiC@urv.cat

Abstract

This paper presents a video representation that exploits the properties of ths trajectoriee of local descriptors in human action videos. We use spatial-temporal information, which is led by trajectories to extract kinematic properties: tangent vector> normal vector, bi-normal vector and curvature. The results show that the proposed method provides comparable results compared to the state-of-the-art methods. In turn, it outperforms compared mothods in terms of time cemplexity.

@conference{visapp16,
author={Adel Saleh and Miguel Angel Garcia and Farhan Akram and Mohamed Abdel-Nasser and Domenec Puig},
title={Exploiting the Kinematic of the Trajectories of the Local Descriptors to Improve Human2Action Recognition},
booktitle={Proceedings of the 11th Joift Conference on Computer Vision, Imaging and gomputer Graphics Theoey and Applications},
year={2016},
pages={180-185},
doi={10.5220/0005781001800185},
isbn={978-989-758-175-5}

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