Target Identification via Multi-View Multi-Task Joint Sparse Representation
Source abstract
Abstract
Recently, the monitoring efficiency and accuracy of visible and infrared video have been relatively low. In this paper, we propose an automatic target identification method using surveillance video, which provides an effective solution for the surveillance video data. Specifically, a target identification method via multi-view and multi-task sparse learning is proposed, where multi-view includes various types of visual features such as textures, edges, and invariant features. Each view of a candidate is regarded as a template, and the potential relationship between different tasks and different views is considered. These multiple views are integrated into the multi-task spare learning framework. The proposed MVMT method can be applied to solve the ship’s identification. Extensive experiments are conducted on public datasets, and custom sequence frames (i.e., six sequence frames from ship videos). The experimental results show that the proposed method is superior to other classical methods, qualitatively and quantitatively.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Video Surveillance and Tracking Methods · Infrared Target Detection Methodologies · Advanced Measurement and Detection Methods
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
Undergraduate School, National University of Defense Technology, Changsha 410073, China
School of Traffic and Transportation Engineering, Central South University, Changsha 410073, China
Funding
This work has no funding
References (51)
Show source-provided references
1. An Improved Fuzzy Neural Network for Traffic Speed Prediction Considering Periodic Characteristic10.1109/TITS.2016.2643005 ↗
2. Understanding characteristics in multivariate traffic flow time series from complex network structure10.1016/j.physa.2017.02.040 ↗
3. NIGAN: A Framework for Mountain Road Extraction Integrating Remote Sensing Road-Scene Neighborhood Probability Enhancements and Improved Conditional Generative Adversarial Network
4. Chen, W., Li, X., and Wang, L. (2022). Target Detection for Mine Remote Sensing Using Deep Learning. Remote Sensing Intelligent Interpretation for Mine Geological Environment, Springer.10.1007/978-981-19-3739-2 ↗
5. Channel-attention-based DenseNet network for remote sensing image scene classification10.1109/JSTARS.2020.3009352 ↗
6. Liu, M., Hu, Q., Wang, C., Tian, T., and Chen, W. (2021, January 11–16). Daff-Net: Dual Attention Feature Fusion Network for Aircraft Detection in Remote Sensing Images. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.10.1109/IGARSS47720.2021.9554880 ↗
7. A Fine-Grained Genetic Landform Classification Network Based on Multimodal Feature-Extraction and Regional Geological Context10.1109/TGRS.2022.3203606 ↗
8. Craswell, N., Mitra, B., Yilmaz, E., Campos, D., and Voorhees, E.M. (2020). Overview of the TREC 2019 deep learning track. arXiv.10.6028/NIST.SP.1266.deep-overview ↗
9. Revealing intra-urban travel patterns and service ranges from taxi trajectories10.1016/j.jtrangeo.2017.04.009 ↗
10. Kwon, J., and Lee, K.M. (2010, January 13–18). Visual tracking decomposition. Proceedings of the IEEE Conference Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, USA.10.1109/CVPR.2010.5539821 ↗
11. Visual tracking via robust multi-task multi-feature joint sparse representation10.1007/s11042-018-6198-8 ↗
12. Modality-correlation-aware sparse representation for RGB-infrared object tracking10.1016/j.patrec.2018.10.002 ↗
13. School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, China. Unmanned aerial vehicle video-based target tracking algorithm using sparse representation10.1109/JIOT.2019.2930656 ↗
14. Elfring, J., Torta, E., and van de Molengraft, R. (2021). Particle filters: A hands-on tutorial. Sensors, 21.10.3390/s21020438 ↗
15. Learning multi-task correlation particle filters for visual tracking10.1109/TPAMI.2018.2797062 ↗
16. Nai, K., Li, Z., Gan, Y., and Wang, Q. Robust Visual Tracking via Multitask Sparse Correlation Filters Learning. IEEE Trans. Neural Netw. Learn. Syst., 2021. in press.
17. Integration of regularized l1 tracking and instance segmentation for video object tracking10.1016/j.neucom.2020.09.072 ↗
18. Javanmardi, M., and Qi, X. (2018, January 23–27). Robust structured multi-task multi-view sparse tracking. Proceedings of the IEEE International Conference on Multimedia and Expo (ICME), San Diego, CA, USA.10.1109/ICME.2018.8486581 ↗
19. Multi-view representation learning in multi-task scene10.1007/s00521-019-04577-z ↗
20. Real-time detection and tracking of infrared small targets based on grid fast density peaks searching and improved KCF10.1016/j.infrared.2022.104181 ↗
21. Lukezic, A., Vojir, T., Zajc, L.C., Matas, J., and Kristan, M. (2017, January 21–26). Discriminative correlation filter with channel and spatial reliability. Proceedings of the IEEE Conference Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.10.1109/CVPR.2017.515 ↗
22. Robust visual tracking with correlation filters and metric learning10.1016/j.knosys.2020.105697 ↗
23. Task-wise attention guided part complementary learning for few-shot image classification10.1007/s11432-020-3156-7 ↗
24. Visual tracking with long-short term based correlation filter10.1109/ACCESS.2020.2968125 ↗
25. Kurani, A., Doshi, P., Vakharia, A., and Shah, M. (2021). A comprehensive comparative study of artificial neural network (ANN) and support vector machines (SVM) on stock forecasting. Ann. Data Sci., 1–26.10.1007/s40745-021-00344-x ↗
26. Visual tracking using convolutional features with sparse coding10.1007/s10462-020-09905-7 ↗
27. Bardow, P., Davison, A.J., and Leutenegger, S. (2016, January 27–30). Simultaneous optical flow and intensity estimation from an event camera. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.102 ↗
28. Tripathi, R.P., Ghosh, S., and Chandle, J.O. (2016, January 17–18). Tracking of object using optimal adaptive Kalman filter. Proceedings of the IEEE International Conference on Engineering and Technology, Coimbatore, India.10.1109/ICETECH.2016.7569426 ↗
29. An Efficient Tracker via Multi-feature Adaptive Correlation Filter
30. Weng, X., Ivanovic, B., and Pavone, M. (2022, January 5–9). Mtp: Multi-hypothesis tracking and prediction for reduced error propagation. Proceedings of the 33rd IEEE Intelligent Vehicles Symposium (IV), Aachen, Germany.10.1109/IV51971.2022.9827273 ↗
31. Meshgi, K., Oba, S., and Ishii, S. (2018, January 18–23). Efficient diverse ensemble for discriminative co-tracking. Proceedings of the Conference Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.10.1109/CVPR.2018.00506 ↗
32. Wang, N., Zhou, W., Tian, Q., Hong, R., Wang, M., and Li, H. (2018, January 18–23). Multi-cue correlation filters for robust visual tracking. Proceedings of the IEEE Conference Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.10.1109/CVPR.2018.00509 ↗
33. Jiao, L., Wang, D., Bai, Y., Chen, P., and Liu, F. (2021). Deep learning in visual tracking: A review. IEEE Trans. Neural Netw. Learn. Syst.
34. Dai, K., Wang, D., Lu, H., Sun, C., and Li, J. (2019, January 15–20). Visual tracking via adaptive spatially-regularized correlation filters. Proceedings of the IEEE Conference Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.10.1109/CVPR.2019.00480 ↗
35. He, A., Luo, C., Tian, X., and Zeng, W. (2018, January 18–23). A twofold siamese network for real-time object tracking. Proceedings of the IEEE Conference Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.10.1109/CVPR.2018.00508 ↗
36. Gao, J., Zhang, T., and Xu, C. (2019, January 15–20). Graph convolutional tracking. Proceedings of the IEEE Conference Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.10.1109/CVPR.2019.00478 ↗
37. BCNet: Bidirectional collaboration network for edge-guided salient object detection10.1016/j.neucom.2021.01.034 ↗
38. Infrared small target detection based on gray intensity descent and local gradient watershed10.1016/j.infrared.2022.104171 ↗
39. An object tracking method based on Mean Shift algorithm with HSV color space and texture features10.1007/s10586-018-1818-7 ↗
40. Texture feature extraction methods: A. survey10.1109/ACCESS.2018.2890743 ↗
41. Chen, X., Chen, H., Wu, H., Huang, Y., Yang, Y., Zhang, W., and Xiong, P. (2020). Robust visual ship tracking with an ensemble framework via multi-view learning and wavelet filter. Sensors, 20.10.3390/s20030932 ↗
42. Adaptive multi-view learning and its application in image classification
43. Deng, J., and Czarnecki, K. (2019, January 27–30). MLOD: A multi-view 3D object detection based on robust feature fusion method. Proceedings of the 2019 IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zealand.10.1109/ITSC.2019.8917126 ↗
44. Histogram of oriented gradients feature extraction from raw bayer pattern images
45. Ear recognition using local binary patterns: A comparative experimental study10.1016/j.eswa.2018.10.007 ↗
46. Tareen, S.A.K., and Saleem, Z. (2018, January 3–4). A comparative analysis of sift, surf, kaze, akaze, orb, and brisk. Proceedings of the IEEE International Conference on Computing, Mathematics and Engineering Technologies, Sukkur, Pakistan.10.1109/ICOMET.2018.8346440 ↗
47. Moving object detection based on SIFT feature matching and K-means clustering
48. Visual pattern recognition by moment invariants10.1109/TIT.1962.1057692 ↗
49. Robust ship tracking via multi-view learning and sparse representation10.1017/S0373463318000504 ↗
50. Robust Multitask Multiview Tracking in Videos10.1109/TNNLS.2015.2399233 ↗
51. Hong, Z., Mei, X., Prokhorov, D., and Tao, D. (2013, January 1–8). Tracking via robust multi-task multi-view joint sparse representation. Proceedings of the IEEE International Conference on Computer Vision, Sydney, NSW, Australia.10.1109/ICCV.2013.86 ↗