TY - GEN
T1 - Recent Progress in Computer-Vision-Based Human Activity Recognition and Related Areas
AU - Rigoll, Gerhard
N1 - Publisher Copyright:
© Springer Nature Switzerland AG, 2019.
PY - 2019
Y1 - 2019
N2 - This paper presents some more recent developments in the research area of human activity recognition. In many cases, human activity recognition can be achieved with the support of body sensors that deliver information about the motion of a person or about acceleration and position. Such data can contribute substantially to the accuracy of the results, however wearing extra equipment is not a very popular practice for most human users and therefore camera-based methods are mostly preferred in today’s systems, which is also a result of the tremendous progress achieved in recent years in pattern recognition and machine learning. Therefore, also this paper concentrates on camera-based methods for human activity recognition. Activity recognition has a large variety of sub-research areas and has many exciting application areas, such as e.g. interaction in smart spaces, surveillance, human-robot interaction or automatic analysis of interest, emotions and human traits.
AB - This paper presents some more recent developments in the research area of human activity recognition. In many cases, human activity recognition can be achieved with the support of body sensors that deliver information about the motion of a person or about acceleration and position. Such data can contribute substantially to the accuracy of the results, however wearing extra equipment is not a very popular practice for most human users and therefore camera-based methods are mostly preferred in today’s systems, which is also a result of the tremendous progress achieved in recent years in pattern recognition and machine learning. Therefore, also this paper concentrates on camera-based methods for human activity recognition. Activity recognition has a large variety of sub-research areas and has many exciting application areas, such as e.g. interaction in smart spaces, surveillance, human-robot interaction or automatic analysis of interest, emotions and human traits.
KW - Activity recognition
KW - Computer Vision
KW - Gait recognition
KW - Gesture recognition
KW - Machine learning
KW - Object tracking
UR - http://www.scopus.com/inward/record.url?scp=85076705842&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-35430-5_1
DO - 10.1007/978-3-030-35430-5_1
M3 - Conference contribution
AN - SCOPUS:85076705842
SN - 9783030354299
T3 - Communications in Computer and Information Science
SP - 3
EP - 7
BT - Pattern Recognition and Information Processing - 14th International Conference, PRIP 2019, Revised Selected Papers
A2 - Ablameyko, Sergey V.
A2 - Krasnoproshin, Viktor V.
A2 - Lukashevich, Maryna M.
PB - Springer
T2 - 14th International conference on Pattern Recognition and Information Processing, PRIP 2019
Y2 - 21 May 2019 through 23 May 2019
ER -