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008			221119s2022 si | s |||| 0|eng d
020			_a9789811922909
024	7		_a10.1007/978-981-19-2290-9 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTK7882.P7 _b2022 EB
100	1		_aHu, Zhongxu _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9685379
245	1	0	_aVision-Based Human Activity Recognition _cby Zhongxu Hu, Chen Lv
250			_aFirst edition 2022
264		1	_aSingapore _bSpringer International Publising _c2022
300			_a1 recurso en línea (X, 121 páginas) _b60 ilustraciones, 57 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSpringerBriefs in Intelligent Systems Artificial Intelligence Multiagent Systems and Cognitive Robotics _x2196-5498
505	0		_a1. Introduction -- 2. Vision-based hand gesture recognition -- 3. Vision-based facial state recognition -- 4. Vision-based body activity recognition -- 5. Human attention modelling -- 6. Conclusion and future work.
520			_aThis book offers a systematic, comprehensive, and timely review on V-HAR, and it covers the related tasks, cutting-edge technologies, and applications of V-HAR, especially the deep learning-based approaches. The field of Human Activity Recognition (HAR) has become one of the trendiest research topics due to the availability of various sensors, live streaming of data and the advancement in computer vision, machine learning, etc. HAR can be extensively used in many scenarios, for example, medical diagnosis, video surveillance, public governance, also in human-machine interaction applications. In HAR, various human activities such as walking, running, sitting, sleeping, standing, showering, cooking, driving, abnormal activities, etc., are recognized. The data can be collected from wearable sensors or accelerometer or through video frames or images; among all the sensors, vision-based sensors are now the most widely used sensors due to their low-cost, high-quality, and unintrusive characteristics. Therefore, vision-based human activity recognition (V-HAR) is the most important and commonly used category among all HAR technologies. The addressed topics include hand gestures, head pose, body activity, eye gaze, attention modeling, etc. The latest advancements and the commonly used benchmark are given. Furthermore, this book also discusses the future directions and recommendations for the new researchers.
988			_aSpringer_Computer_2022
650		7	_2embne _9668197 _aDetectores de proximidad
650		7	_2embne _9155848 _aInteracción hombre-ordenador
700	1		_aLv, Chen _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9685380
776	0	8	_iPrinted edition: _z9789811922893
776	0	8	_iPrinted edition: _z9789811922916
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-19-2290-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b11/2022 _dz _eIG _zSI