Table 2.

Frame-level anomaly detection running time comparison

Method	Platform	CPU	GPU	Running time (seconds per frame)
				UCSD Ped1	UCSD Ped2
MDT [6]	–	3.0 GHz	–	25	–
AMDN [3]	MATLAB	2.1 GHz	Nvidia Quadro K4000	5.2	7.5
XU et al. [61] without GPU	Pytorch	2.1 GHz	–	2.68	6.84
Hierarchical framework [62]	MATLAB	3.0 GHz	–	5	5
ST-CNN [63]	Caffe	2.8 GHz	–	0.37	0.39
AED [64]	N/A	N/A	N/A	0.073	–
ICN [65]	Tensorflow	2.4 GHz	NVIDIA Tesla K40c	–	0.18
XU et al. [61] with GPU	Pytorch	2.1 GHz	Nvidia TITAN X	0.00242	0.00265
Two-Stream R-ConvVAE [4]	Tensorflow	2.6 GHz	Nvidia TITAN X	0.0012	–
Oursa	Tensorflow	2.6 GHz	Nvidia V100, Nvidia TITAN Xp	$t_{\mathit{pre}}+2.18\times {10}^{-5}$	$t_{\mathit{pre}}+$ $3.71\times {10}^{-5}$

^aOur pre-trained model running time$t_{\mathit{pre}}=0.067$