{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T16:09:53Z","timestamp":1772554193548,"version":"3.50.1"},"reference-count":47,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"4","license":[{"start":{"date-parts":[[2018,10,1]],"date-time":"2018-10-01T00:00:00Z","timestamp":1538352000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Robot. Autom. Lett."],"published-print":{"date-parts":[[2018,10]]},"DOI":"10.1109\/lra.2018.2856261","type":"journal-article","created":{"date-parts":[[2018,7,16]],"date-time":"2018-07-16T22:00:57Z","timestamp":1531778457000},"page":"3709-3716","source":"Crossref","is-referenced-by-count":71,"title":["Distributed Perception by Collaborative Robots"],"prefix":"10.1109","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8731-1084","authenticated-orcid":false,"given":"Ramyad","family":"Hadidi","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0079-2146","authenticated-orcid":false,"given":"Jiashen","family":"Cao","sequence":"additional","affiliation":[]},{"given":"Matthew","family":"Woodward","sequence":"additional","affiliation":[]},{"given":"Michael S.","family":"Ryoo","sequence":"additional","affiliation":[]},{"given":"Hyesoon","family":"Kim","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1145\/1460096.1460144"},{"key":"ref38","article-title":"An\n analysis of deep neural network models for practical applications","author":"canziani","year":"2016"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2017.7989324"},{"key":"ref32","first-page":"4255","article-title":"Privacy-preserving human\n activity recognition from extreme low resolution.","author":"ryoo","year":"0","journal-title":"Proc Assoc Advance Artif Intell"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2015.7353481"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2015.7139361"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/3079856.3080244"},{"key":"ref34","article-title":"Learning robot activities from first-person human videos using convolutional future regression","author":"lee","year":"0","journal-title":"Proc IEEE\/RSJ Int Conf Intell Robots Syst"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/3007787.3001164"},{"key":"ref40","first-page":"363","author":"farneb\u00e4ck","year":"2003","journal-title":"Two-frame Motion Estimation Based on Polynomial Expansion"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3037697.3037698"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2014.6855235"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS.2017.226"},{"key":"ref14","article-title":"Embedded Learning Library (ELL)","year":"2017"},{"key":"ref15","first-page":"525","article-title":"XNOR-Net: Imagenet classification using\n binary convolutional neural networks","author":"rastegari","year":"0","journal-title":"Proc Eur Conf Comput Vision"},{"key":"ref16","article-title":"Mobilenets: Efficient convolutional neural\n networks for mobile vision applications","author":"howard","year":"2017"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/2906388.2906396"},{"key":"ref18","article-title":"Caffe2Go: Delivering real-time AI in the palm of your hand","year":"2017"},{"key":"ref19","article-title":"Introduction to TensorFlow Lite","year":"0"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.23919\/DATE.2017.7927211"},{"key":"ref4","article-title":"Extreme\n low resolution activity recognition with multi-siamese embedding learning","author":"ryoo","year":"0","journal-title":"Proc Assoc Advance Artif Intell"},{"key":"ref27","article-title":"NVIDIA TK","year":"2017"},{"key":"ref3","article-title":"Neural\n machine translation by jointly learning to align and translate","author":"bahdanau","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/2966986.2967068"},{"key":"ref29","first-page":"1223","article-title":"Large scale distributed deep networks","author":"dean","year":"0","journal-title":"Proc Proc Conf Neural Inf Process Syst"},{"key":"ref5","first-page":"568","article-title":"Two-Stream convolutional networks for action recognition in videos","author":"simonyan","year":"0","journal-title":"Proc Conf Neural Inf Process Syst"},{"key":"ref8","first-page":"168","article-title":"Embedded binarized neural networks","author":"mcdanel","year":"0","journal-title":"Proc Int Conf Wireless Netw Embedded Syst"},{"key":"ref7","article-title":"Compression of deep convolutional neural networks for fast and low power mobile applications","author":"kim","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390177"},{"key":"ref9","first-page":"250","article-title":"14.7 A 288 \n$\\mu$W programmable deep-learning processor with 270\ufffdKB\n on-chip weight storage using non-uniform memory hierarchy for mobile intelligence","author":"bang","year":"0","journal-title":"Proc Int Solid-State Circuits Conf"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"ref46","article-title":"TensorFlow: Large-scale machine learning on\n heterogeneous systems","author":"abadi","year":"2015"},{"key":"ref20","article-title":"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and \n$<$0.5 MB model size","author":"iandola","year":"2016"},{"key":"ref45","article-title":"Keras","author":"chollet","year":"2015"},{"key":"ref22","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1145\/3229762.3229765"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref42","first-page":"1337","article-title":"Deep learning with COTS HPC systems","author":"coates","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref24","article-title":"GoPiGo Robot","author":"industries","year":"0"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2011.6126543"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref44","article-title":"NVIDIA Jetson TX","year":"2017"},{"key":"ref26","article-title":"Raspberry Pi 3","author":"foundation","year":"0"},{"key":"ref43","article-title":"Apache Avro","author":"foundation","year":"0"},{"key":"ref25","article-title":"Raspberry Pi 3","author":"foundation","year":"2017"}],"container-title":["IEEE Robotics and Automation Letters"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/7083369\/8386768\/08411096.pdf?arnumber=8411096","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T16:22:42Z","timestamp":1642004562000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8411096\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,10]]},"references-count":47,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.1109\/lra.2018.2856261","relation":{},"ISSN":["2377-3766","2377-3774"],"issn-type":[{"value":"2377-3766","type":"electronic"},{"value":"2377-3774","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,10]]}}}