TY - GEN
T1 - SATJiP
T2 - 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024
AU - Shen, Liheng
AU - Matsukawa, Tetsu
AU - Suzuki, Einoshin
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - Video Anomaly Detection (VAD) is a significant task, which refers to taking a video clip as input and outputting class labels, e.g., normal or abnormal, at the frame level. Wang et al. proposed a method called DSTJiP, which trains the model by solving Decoupled Spatial and Temporal Jigsaw Puzzles and achieves impressive VAD performance. However, the model sometimes fails to detect abnormal human actions where abnormal motions are accompanied by normal motions. The reason is that the model learns representations of little- and non-motion parts of training examples, resulting in being insensitive to abnormal motions. To circumvent this problem, we propose to solve Spatial and Augmented Temporal Jigsaw Puzzles (SATJiP) as an extension of DSTJiP. SATJiP encourages the model to focus on motions by a novel pretext task, enabling it to detect abnormal motions accompanied by normal motions. Experiments conducted on three standard VAD benchmarks demonstrate that SATJiP outperforms the state-of-the-art methods.
AB - Video Anomaly Detection (VAD) is a significant task, which refers to taking a video clip as input and outputting class labels, e.g., normal or abnormal, at the frame level. Wang et al. proposed a method called DSTJiP, which trains the model by solving Decoupled Spatial and Temporal Jigsaw Puzzles and achieves impressive VAD performance. However, the model sometimes fails to detect abnormal human actions where abnormal motions are accompanied by normal motions. The reason is that the model learns representations of little- and non-motion parts of training examples, resulting in being insensitive to abnormal motions. To circumvent this problem, we propose to solve Spatial and Augmented Temporal Jigsaw Puzzles (SATJiP) as an extension of DSTJiP. SATJiP encourages the model to focus on motions by a novel pretext task, enabling it to detect abnormal motions accompanied by normal motions. Experiments conducted on three standard VAD benchmarks demonstrate that SATJiP outperforms the state-of-the-art methods.
UR - http://www.scopus.com/inward/record.url?scp=85192558837&partnerID=8YFLogxK
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U2 - 10.1007/978-981-97-2242-6_3
DO - 10.1007/978-981-97-2242-6_3
M3 - Conference contribution
AN - SCOPUS:85192558837
SN - 9789819722419
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 27
EP - 40
BT - Advances in Knowledge Discovery and Data Mining - 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, Proceedings
A2 - Yang, De-Nian
A2 - Xie, Xing
A2 - Tseng, Vincent S.
A2 - Pei, Jian
A2 - Huang, Jen-Wei
A2 - Lin, Jerry Chun-Wei
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 7 May 2024 through 10 May 2024
ER -