TY - JOUR
T1 - Survey of Spatio-Temporal Interest Point Detection Algorithms in Video
AU - Li, Yanshan
AU - Xia, Rongjie
AU - Huang, Qinghua
AU - Xie, Weixin
AU - Li, Xuelong
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2017
Y1 - 2017
N2 - Recently, increasing attention has been paid to the detection of spatio-temporal interest points (STIPs), which has become a key technique and research focus in the field of computer vision. Its applications include human action recognition, video surveillance, video summarization, and content-based video retrieval. Amount of work has been done by many researchers in STIP detection. This paper presents a comprehensive review on STIP detection algorithms. We first propose the detailed introductions and analysis of the existing STIP detection algorithms. STIP detection algorithms are robust in detecting interest points for video in the spatio-temporal domain. Next, we summarize the existing challenges in the STIP detection for video, such as low time efficiency, poor robustness with respect to camera movement, illumination change, perspective occlusion, and background clutter. This paper also presents the application situations of STIP and discusses the potential development trends of STIP detection.
AB - Recently, increasing attention has been paid to the detection of spatio-temporal interest points (STIPs), which has become a key technique and research focus in the field of computer vision. Its applications include human action recognition, video surveillance, video summarization, and content-based video retrieval. Amount of work has been done by many researchers in STIP detection. This paper presents a comprehensive review on STIP detection algorithms. We first propose the detailed introductions and analysis of the existing STIP detection algorithms. STIP detection algorithms are robust in detecting interest points for video in the spatio-temporal domain. Next, we summarize the existing challenges in the STIP detection for video, such as low time efficiency, poor robustness with respect to camera movement, illumination change, perspective occlusion, and background clutter. This paper also presents the application situations of STIP and discusses the potential development trends of STIP detection.
KW - STIP detection algorithm
KW - Video
KW - local invariant feature
KW - spatio-temporal interest point (STIP)
UR - https://www.scopus.com/pages/publications/85028380380
U2 - 10.1109/ACCESS.2017.2712789
DO - 10.1109/ACCESS.2017.2712789
M3 - 文章
AN - SCOPUS:85028380380
SN - 2169-3536
VL - 5
SP - 10323
EP - 10331
JO - IEEE Access
JF - IEEE Access
M1 - 7944559
ER -