Anomaly Event Detection Based on Two-Stream Network and Multi-instance Learning

Fig. 3. Comparison charts of qualitative results for video frame tests of various anomaly events. (a) Explosion; (b) animal abuse; (c) traffic accident; (d) normal video frame fragment

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Fig. 4. Score variations of anomaly video clips under different iteration times. (a) 2000 times; (b) 5000 times; (c) 8000 times; (d) 12000 times

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Table 1. Configuration of experimental environment

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Table 1. Configuration of experimental environment

Experimental environment	Detailed information
	CPU: Intel(R) Core(TM) i7-8700 at 3.20 GHz
Computer	RAM: 16 GB
	MATLAB 2017
	Python 2.7
	GPU: NVIDIA GeForce GTX 1060 3GB
Server	TensorFlow 1.6
	Display memory: 12 GB
	Ubuntu 14.04

Table 2. Comparison of experimental results of two models

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Table 2. Comparison of experimental results of two models

Model	Network	AUC	Detection speed / fps
	Time flow: RGB	71.43
I3D	Spatial flow: TV-L1	73.61
	Two-stream fusion model: TV-L1+RGB	76.83	25
	Time flow: RGB	71.43
M-I3D	Spatial flow: MotionNet	74.84
	Two-stream fusion model: MotionNet +RGB	77.25	32

Table 3. Comparisons of the experimental results between the proposed method and other methods

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Table 3. Comparisons of the experimental results between the proposed method and other methods

Method	EER /%	AUC	Detection speed /fps
Method in Ref. [23]	34.9	50.66
Method in Ref. [5]	27.3	65.51	143.50
Method in Ref. [25]	23.6	69.30	0.04
Method in Ref. [21]		75.41
Method in Ref. [24]	18.1	73.68	120.00
Our method	16.8	77.25	32.00

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Xianbin Yang, Jianwu Dang, Song Wang, Yangping Wang. Anomaly Event Detection Based on Two-Stream Network and Multi-instance Learning[J]. Laser & Optoelectronics Progress, 2021, 58(20): 2015006

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Paper Information

Category: Machine Vision

Received: Nov. 25, 2020

Accepted: Jan. 20, 2021

Published Online: Oct. 14, 2021

The Author Email: Dang Jianwu (dangjw@mail.lzjt.cn)

DOI:10.3788/LOP202158.2015006

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology