Multi-Target Tracking Algorithm Based on Improved Markov Random Field Model of Optical Flow Motion Constraint

Yuhui Niu; Zhenghao Xi; Yajing Xue; Jianchao Chen

doi:10.3788/LOP202259.0215006

Journals >Laser & Optoelectronics Progress >Volume 59 >Issue 2 >Page 0215006 > Article

Laser & Optoelectronics Progress
Vol. 59, Issue 2, 0215006 (2022)

Multi-Target Tracking Algorithm Based on Improved Markov Random Field Model of Optical Flow Motion Constraint

Yuhui Niu^*, Zhenghao Xi, Yajing Xue^**, and Jianchao Chen

Author Affiliations

School of Electrical and Electronic Engineering, Shanghai University of Engineering Science, Shanghai 201620, China

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DOI: 10.3788/LOP202259.0215006 Cite this Article Set citation alerts

Yuhui Niu, Zhenghao Xi, Yajing Xue, Jianchao Chen. Multi-Target Tracking Algorithm Based on Improved Markov Random Field Model of Optical Flow Motion Constraint[J]. Laser & Optoelectronics Progress, 2022, 59(2): 0215006 Copy Citation Text

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Fig. 1. Flow chart of tracking process

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Fig. 2. Markov random field

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Fig. 3. Target motion vector representation

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Fig. 4. Tracking results of sequence KITTI-17. (a) 1st frame; (b) 8th frame; (c) 14th frame;

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Fig. 5. Tracking results of sequence PETS09-S2L1. (a) 430th frame; (b) 462nd frame; (c) 474th frame;

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Fig. 6. Tracking results of sequence KITTI-TEST17. (a) 9th frame; (b) 22nd frame; (c) 37th frame; (d) 56th frame;

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Sequence	Algorithm	MOTA /%	MOTP /%	IDF1 /%	FN	FP	IDSW
KITTI-17	Algorithm in reference ［20］	65.2	72.7	78.2	130	32	13
	Algorithm in reference ［21］	77.6	81.3	71.3	89	44	20
	Algorithm in reference ［22］	79.8	82.1		147	53	32
	Proposed algorithm	83.9	87.6	80.7	96	38	6
PETS09-S2L1	Algorithm in reference ［20］	57.6	65.5	76.3	609	265	24
	Algorithm in reference ［21］	85.3	82.8	79.6	279	185	10
	Algorithm in reference ［22］	87.4	76.6		400	123	62
	Proposed algorithm	89.6	84.4	83.0	232	156	9

Table 1. Performance comparison of different algorithms

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