Multi-Moving Object Detection Based on Edge Multi-Channel Gradient Model

Fig. 2. Detection results of different algorithms. (a) Original image; (b) algorithm of Ref. [18]; (c) algorithm of Ref. [19]; (d) algorithm of Ref. [20]; (e) our algorithm

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Fig. 3. Detection error rates of different algorithms

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Table 1. Parameter of the video sequence
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Table 1. Parameter of the video sequence
Number Video sequence Frame Size
1 PETS2006 1200 720×576
2 blizzard 7000 720×480
3 David 770 320×240

Table 2. Detection performance of different algorithms

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Table 2. Detection performance of different algorithms

Algorithm	Video1				Video2				Video3
Algorithm	R/%	P/%	X_{F_M}/%	FPS/frame	R/%	P/%	X_{F_M}/%	FPS/frame	R/%	P/%	X_{F_M}/%	FPS/frame
Ref.[18]	87.3	90.6	88.9	2	79.8	82.1	80.9	3	76.9	70.5	73.6	14
Ref.[19]	98.8	78.9	87.7	1	71.5	85.8	78.0	5	78.2	69.9	73.8	11
Ref. [20]	77.8	85.7	81.6	4	79.7	74.8	77.2	7	70.5	73.8	72.1	13
Ours	93.3	88.1	90.6	26	87.4	83.5	85.4	32	83.5	79.8	81.6	43

Table 3. Amount of data calculated and store in each stage

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Table 3. Amount of data calculated and store in each stage

Memory consuming and computational demanding stage	Data store
Establishment of the E-McGM	(N-B)×n_x×n_y×n_CF×n_TF×n_SF×m_θ
Computing A^TA	(N-B)×n_x×n_y×m_θ×6
Velocity responses in m directions around a point	(N-B)×n_x×n_y×m_θ×4
Computing velocity magnitude and direction	(N-Bl)×n_x×n_y

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Jieyu Chen, Zhenghao Xi, Junxin Lu. Multi-Moving Object Detection Based on Edge Multi-Channel Gradient Model[J]. Laser & Optoelectronics Progress, 2021, 58(4): 0415002

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