Survey of Spatio-Temporal Interest Point Detection Algorithms in Video

Yanshan Li; Rongjie Xia; Qinghua Huang; Weixin Xie; Xuelong Li

doi:10.1109/ACCESS.2017.2712789

Survey of Spatio-Temporal Interest Point Detection Algorithms in Video

Yanshan Li
, Rongjie Xia
, Qinghua Huang
, Weixin Xie
, Xuelong Li

School of Artificial Intelligence, OPtics and Electronics

Shenzhen University
South China University of Technology
CAS - Xi'an Institute of Optics and Precision Mechanics

Research output: Contribution to journal › Article › peer-review

53 Scopus citations

Abstract

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.

Original language	English
Article number	7944559
Pages (from-to)	10323-10331
Number of pages	9
Journal	IEEE Access
Volume	5
DOIs	https://doi.org/10.1109/ACCESS.2017.2712789
State	Published - 2017

Keywords

STIP detection algorithm
Video
local invariant feature
spatio-temporal interest point (STIP)

Access to Document

10.1109/ACCESS.2017.2712789

Cite this

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title = "Survey of Spatio-Temporal Interest Point Detection Algorithms in Video",

abstract = "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.",

keywords = "STIP detection algorithm, Video, local invariant feature, spatio-temporal interest point (STIP)",

author = "Yanshan Li and Rongjie Xia and Qinghua Huang and Weixin Xie and Xuelong Li",

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year = "2017",

doi = "10.1109/ACCESS.2017.2712789",

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TY - JOUR

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AU - Li, Yanshan

AU - Xia, Rongjie

AU - Huang, Qinghua

AU - Xie, Weixin

AU - Li, Xuelong

PY - 2017

Y1 - 2017

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KW - local invariant feature

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Survey of Spatio-Temporal Interest Point Detection Algorithms in Video

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Keywords

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