A Specific Algorithm Based on Motion Direction Prediction
In this paper, we study the estimation of motion direction prediction for fast motion and propose a threshold-based human target detection algorithm using motion vectors and other data as human target feature information. The motion vectors are partitioned into regions by normalization to form a mot...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6678596 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832568524825952256 |
|---|---|
| author | Zhesen Chu Min Li |
| author_facet | Zhesen Chu Min Li |
| author_sort | Zhesen Chu |
| collection | DOAJ |
| description | In this paper, we study the estimation of motion direction prediction for fast motion and propose a threshold-based human target detection algorithm using motion vectors and other data as human target feature information. The motion vectors are partitioned into regions by normalization to form a motion vector field, which is then preprocessed, and then the human body target is detected through its motion vector region block-temporal correlation to detect the human body motion target. The experimental results show that the algorithm is effective in detecting human motion targets in videos with the camera relatively stationary. The algorithm predicts the human body position in the reference frame of the current frame in the video by forward mapping the motion vector of the current frame, then uses the motion vector direction angle histogram as a matching feature, and combines it with a region matching strategy to track the human body target in the predicted region, thus realizing the human body target tracking effect. The algorithm is experimentally proven to effectively track human motion targets in videos with relatively static backgrounds. To address the problem of sample diversity and lack of quantity in a multitarget tracking environment, a generative model based on the conditional variational self-encoder conditional generation of adversarial networks is proposed, and the performance of the generative model is verified using pedestrian reidentification and other datasets, and the experimental results show that the method can take advantage of the advantages of both models to improve the quality of the generated results. |
| format | Article |
| id | doaj-art-c3824f73157041efa4449da7c87f0517 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-c3824f73157041efa4449da7c87f05172025-02-03T00:58:58ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/66785966678596A Specific Algorithm Based on Motion Direction PredictionZhesen Chu0Min Li1School of Physical Education (Main Campus), Zhengzhou University, Zhengzhou, Henan 450001, ChinaEnglish and Economic and Trade Department, Jiaozuo Teachers’ College, Jiaozuo, Henan 454000, ChinaIn this paper, we study the estimation of motion direction prediction for fast motion and propose a threshold-based human target detection algorithm using motion vectors and other data as human target feature information. The motion vectors are partitioned into regions by normalization to form a motion vector field, which is then preprocessed, and then the human body target is detected through its motion vector region block-temporal correlation to detect the human body motion target. The experimental results show that the algorithm is effective in detecting human motion targets in videos with the camera relatively stationary. The algorithm predicts the human body position in the reference frame of the current frame in the video by forward mapping the motion vector of the current frame, then uses the motion vector direction angle histogram as a matching feature, and combines it with a region matching strategy to track the human body target in the predicted region, thus realizing the human body target tracking effect. The algorithm is experimentally proven to effectively track human motion targets in videos with relatively static backgrounds. To address the problem of sample diversity and lack of quantity in a multitarget tracking environment, a generative model based on the conditional variational self-encoder conditional generation of adversarial networks is proposed, and the performance of the generative model is verified using pedestrian reidentification and other datasets, and the experimental results show that the method can take advantage of the advantages of both models to improve the quality of the generated results.http://dx.doi.org/10.1155/2021/6678596 |
| spellingShingle | Zhesen Chu Min Li A Specific Algorithm Based on Motion Direction Prediction Complexity |
| title | A Specific Algorithm Based on Motion Direction Prediction |
| title_full | A Specific Algorithm Based on Motion Direction Prediction |
| title_fullStr | A Specific Algorithm Based on Motion Direction Prediction |
| title_full_unstemmed | A Specific Algorithm Based on Motion Direction Prediction |
| title_short | A Specific Algorithm Based on Motion Direction Prediction |
| title_sort | specific algorithm based on motion direction prediction |
| url | http://dx.doi.org/10.1155/2021/6678596 |
| work_keys_str_mv | AT zhesenchu aspecificalgorithmbasedonmotiondirectionprediction AT minli aspecificalgorithmbasedonmotiondirectionprediction AT zhesenchu specificalgorithmbasedonmotiondirectionprediction AT minli specificalgorithmbasedonmotiondirectionprediction |