Appearance-motion heterogeneous networks for video anomaly detection

Author:

Li HongjunORCID,Sun XiaohuORCID,Chen MingyiORCID

Funder

National Natural Science Foundation of China

State Key Laboratory of Novel Software Technology

Nantong Science and Technology Program

Postgraduate Research and Practice Innovation Program of Jiangsu Province

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Reference49 articles.

1. Kumar A, Sato Y, Oishi T, Ono S, Ikeuchi K (2014) Improving gps position accuracy by identification of reflected gps signals using range data for modeling of urban structures. Seisan Kenkyu 66(2):101–107

2. Ionescu RT, Smeureanu S, Alexe B, Popescu M (2017) Unmasking the abnormal events in video, in Proc IEEE Int Conf Comput Vis. (ICCV), Venice, Italy, pp. 2914–2922

3. Kumar A, Oishi T, Ono S, Bano A, Ikeuchi K (2013) “Global coordinate adjustment of 3D survey models in world geodetic system under unstable GPS condition,”20th ITS World CongressITS, Japan,14–18

4. Liu W, Luo W, Lian D, Gao S (2018), Future frame prediction for anomaly detection-A new baseline, in Proc IEEE Conf Comput Vis Pattern Recognit (CVPR), Salt Lake City, UT, USA, pp. 6536–6545

5. Luo WX, Liu W, Lian DZ, Tang JH, Duan LX, Peng X, Gao SH (2021) Video anomaly detection with sparse coding inspired deep neural networks. IEEE Trans Pattern Anal Mach Intell 43(3):1070–1084

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