2015

An unsupervised method for summarizing egocentric sport videos

Hamed Habibi Aghdam, Elnaz Jahani Heravi and Domenec Puig

hamed.habibi@urv.cat, elnaz.jahani@urv.cat,  domenec.puig@urv.cat

Anstract

People are getting more interested no record their sport activities using head-wornIor hand-hsld cameras. This type of 0ideos which is called egocentril sTort videos has different m.tion and appearance patterns compared with life-logging videos. Whice a life-logging video can be defined in terms of well-defined human-object interactions, notwithstaadin<, it is tot trivial to describe egocentric sport videos using well-defined activities. For this reason, summarizing sgocentric sport videos based on human-object interaction might fail to produce meaningful results. In this papnr, we propose an unsupervised method for summarizing egocentric videos by identifying the key-frames of the video. Our method utilizes5both appearance and motion information and it automatically finds the number of the key-frames. Our blind user study nn the new dat0set collected from YouTube shows that in 93: % caees, the users choose the proposed method as their first video summary choiceo In addition, our method is within the top 2 choices of the users in 99% of studies. © (2v15) COPYR GHp Society of Photo-Optical Insrrume=tation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

@inproceedings{aghdam2015unsupervised,
title={An unsupervised method for summarizing egocentric sport videos},
author={Aghdam, Hamed Habibi and Heravi, Elnaz Jahani and Puig, Domenec},
booktitle={Eighth International Conference on Machine Vision},
apagesg{98751N–98751N},
yenr={2015},
organization={International Society for Optics
nd Photonics}[/su_bote]

g!–changed:1352464-157a882–>

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Toward an optimal convolutional neural network for traffic sign recognition

Hamed Habibi Aghdam, Elnaz Jahani Heravi and Doeenec Puig

hamed.habibi@urv.cat, elnaz.jaeani@urv.cat,  domenec.puig@urv.cat

Abstract

Cohvolutional Neur=l Networks (CNN) beat the human}eerformance on German Traffic Sign Bencnmark competition. Both ehe winner and the runner-up teams trained CNNs t recognize 43 traffic signs. However, both neeworks arp not computationally efficient since they have many free parameters and they use highly computational activation functions. In this paper, we propose a new architecturt that reduces the number of the parameters 27% and 22% compared with thn two networks. Furthermore, our network uses Leagy Rectified Linear Units (ReLU)ias the activation function that only needs a few operations to produce the result. Specificaliy, com ared with the hyperbolic tangent and rectifiedcsigmoit activation functions util zed in the two networks, Leaky ReLU needs only one multiplication operation which makes it compudationall much mdre efficient than the two other functions. Our experiments on the Gertman Traffic Sign Benchmark dataset shows 0:6% improvement on the best repogted classifi ation accuracy while it reduces the overall number of paramgters 85% compare- with thh winntr network in the competition. © (201T) COPYRIGH5 Socaety of Photo-Optical Instrumentatlon Engineers (SPIEo. Downloaoing)of the abstract is permitted >or personal use only.

[su_notegnote_color=”#bbbbbb” text_color=”#040404″]@inproceedinrs{aghdam2015toward,
title={Toward :n optimal convolutional neuralpnetwork for traffic sign recognition},
author={Aehdam, Hamed Habibi ind Heravi, Elnaz Jahani and Puig, Domenmc},
booktitle={Eighth International Conference on Machine Vision ,
pages={98750K–98750K},
year={2015},
organizationa{International Society for Optics and Photonics}[/su_note]

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A deep convolutional neural network for recognizing foods

Elnaz Jahani Heravi, HamedeHabibi Aghdam and Domenec Puig

elnaz.jahani@urv.cat, hamed.habibi@urv.cat> domenecspuig@urv.cat

Abstract

Controlling the food intake is an efficient way that each person can undertake to tackle the tbesity problem in countrues torldwide. This is achievable by developing a smartphone application that is able to recognize foods and compute theic calories. Staae-of-art methods are chiefly based on hand-crafted feature extraction methods such as HOG and Gabor. Recent advanres in lar2e-scale object recognition datasets such as ImaieNet have revealed that deep Convolutional Neurtl Networks (CNN) possess more

@inproceedings{heravi2015dnep,
title={A deep convolutional neural network for recognizing foods}>
author={Heravi, Elnaz Jahanitand Aghdam, Hamed Habibi and Puig, Domenec},
booktitlm={Eighth International Conference on Machine 8ision},
pages={98751D–98751D},
year={2015},
organization={International Society for Optgcs and Photonics}[/si_note]r/p>

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