Name
Abstract | ||
|---|---|---|
According to the lack of spatio-temporal information of convolution neural network abstraction, an online visual tracking algorithm based on convolution neural network is proposed, combining the spatio-temporal context model to the order filter of convolution neural network. Firstly, the initial target is preprocessed and the target spatial model is extracted, the spatio-temporal context model is obtained by the spatio-temporal information. The first layer adopts the spatio-temporal context model to convolve the input to obtain the simple layer feature. The second layer starts with skip the spatio-temporal context model to get a set of convolution filters, convolving with the simple features of the first layer to extract the target abstract features, and then the deep expression of the target can be obtained by superimposing the convolution results of the simple layer. Finally, the target tracking is realized by sparse updating method combining with particle filter tracking framework. Experiments show that deep abstract feature extracted by online convolution network structure combining with spatio-temporal context model, can preserve spatio-temporal information and improve the background clutters, illumination variation, low resolution, occlusion and scale variation and the tracking efficiency under complex background. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s11042-017-5533-9 | Multimedia Tools Appl. |
Keywords | Field | DocType |
Visual tracking, Spatio-temporal context, Convolutional neural network (CNN), Particle filter | Computer vision,Pattern recognition,Computer science,Convolution,Convolutional neural network,Particle filter,Context model,Eye tracking,Artificial intelligence,Temporal context,Simple Features,Network structure | Journal |
Volume | Issue | ISSN |
78 | 1 | 1573-7721 |
Citations | PageRank | References |
1 | 0.35 | 19 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongxiang Wang | 1 | 6 | 6.12 |
| peizhong liu | 2 | 42 | 7.95 |
| Yongzhao Du | 3 | 11 | 5.61 |
| Xiaofang Liu | 4 | 8 | 2.84 |