Research on Particle Filter Video Tracking Algorithms
Aiming at the problem of insufficient robustness of moving target tracking in complex scenes, a particle filter target tracking algorithm based on CNN feature extraction is proposed CNN selflearning mechanism is used to extract highlevel semantic features of objects in images Chaotic sequence variab...
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| Main Authors: | , |
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| Format: | Article |
| Language: | zho |
| Published: |
Harbin University of Science and Technology Publications
2020-08-01
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| Series: | Journal of Harbin University of Science and Technology |
| Subjects: | |
| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1848 |
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| Summary: | Aiming at the problem of insufficient robustness of moving target tracking in complex scenes, a particle filter target tracking algorithm based on CNN feature extraction is proposed CNN selflearning mechanism is used to extract highlevel semantic features of objects in images Chaotic sequence variable scale firefly algorithm is introduced to improve the accuracy of target recognition A particle filter tracking algorithm combining mean shift and weight optimization is constructed to optimize the weight of particles, improve the diversity of particle sets and make video tracking more accurate The simulation results show that the proposed algorithm can effectively adapt to occlusion, illumination, violent motion and other scenes, and has good adaptability and high realtime performance to illumination change, scale change, occlusion and so on The results of comparison with seven other methods show that under the same experimental conditions, the tracking success rate and tracking accuracy of this method are 5%~40% higher than those of other methods |
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| ISSN: | 1007-2683 |