Chiman Kwan
ABSTRACT
In this paper, we summarize some recent results on objective tracking and classification in infrared and low quality videos using compressive measurements. Two compressive measurement modes were investigated. One was based on subsampling of the original measurements. The other was based on coded aperture camera. It is important to emphasize that conventional trackers require the compressive measurements be reconstructed first before any tracking and classification processing steps begin. The reconstruction is time-consuming and may also lose information. Our proposed approach directly uses compressive measurements and a deep learning tracker known as You Only Look Once (YOLO), which is fast and can track multiple objects simultaneously, was used to track objects. The detected objects are then recognized using another deep learning model called residual network (ResNet). Extensive experiments using infrared videos from long distances were conducted. Results show that the proposed approach performs much better than conventional trackers, which failed to deal with compressive measurements. Instead, ResNet classifier performs better than the built-in classifier in YOLO.
- Berg, A.: Detection and Tracking in Thermal Infrared Imagery. Diss. Linköping University Electronic Press, 2016.Google Scholar
Cross Ref
- Bertinetto, L., Valmadre, J., Golodetz, S., Miksik, O., and Torr, P. 2016. Staple: Complementary Learners for Real-Time Tracking, Conference on Computer Vision and Pattern Recognition.Google Scholar
- Candes, E. J. and Wakin, M. B. 2008. An Introduction to Compressive Sampling, IEEE Signal Processing Magazine.Google Scholar
- He, K., Zhang, X., Ren, S., and Sun, J. 2016. Deep Residual Learning for Image Recognition, Conference on Computer Vision and Pattern Recognition.Google Scholar
- Kandylakis, Z., et al. 2017. Multimodal Data Fusion for Effective Surveillance of Critical Infrastructure, ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 87--93.Google Scholar
- Kwan, C., Chou, B., and Kwan, L. M. 2018. A Comparative Study of Conventional and Deep Learning Target Tracking Algorithms for Low Quality Videos, 15th International Symposium on Neural Networks.Google Scholar
- Kwan, C., Chou, B., Echavarren, A., Budavari, B., Li, J., and Tran, T.: Compressive Vehicle Tracking Using Deep Learning, IEEE Ubiquitous Computing, Electronics & Mobile Communication Conference, New York City, (2018)Google Scholar
- Kwan, C., Chou, B., Yang, J. and Tran, T. 2019. Target Tracking and Classification Directly in Compressive Measurement for Low Quality Videos. Pattern Recognition and Tracking XXX (Conference SI120).Google Scholar
- Kwan, C., Chou, B., Yang, J. and Tran, T. 2019. Compressive object tracking and classification using deep learning for infrared videos. Pattern Recognition and Tracking XXX (Conference SI120).Google Scholar
- Kwan, C., Chou, B., Yang, J., Rangamani, A., Tran, T., Zhang, J., and Etienne-Cummings, R. 2019. Target tracking and classification directly using compressive sensing camera for SWIR videos. Journal of Signal, Image, and Video Processing, June 7.Google ScholarCross Ref
- Kwan, C., Chou, B., Yang, J., Rangamani, A., Tran, T., Zhang, J., and Etienne-Cummings, R. 2019. Target Tracking and Classification Using Compressive Measurements of MWIR and LWIR Coded Aperture Cameras. Journal Signal and Information Processing, August.Google Scholar
- Kwan, C., Chou, B., Yang, J., Rangamani, A., Tran, T., Zhang, J., and Etienne-Cummings, R. 2019. Deep Learning based Target Tracking and Classification for Low Quality Videos Using Coded Aperture Camera. MDPI Sensors. August 28.Google Scholar
- Kwan, C., Yin, J., and Zhou, J. 2018. The Development of a Video Browsing and Video Summary Review Tool, Proc. SPIE 10649, Pattern Recognition and Tracking XXIX, 1064907.Google Scholar
- Kwan, C., Zhou, J., Wang, Z., and Li, B. 2018 Efficient Anomaly Detection Algorithms for Summarizing Low Quality Videos, Proc. SPIE 10649, Pattern Recognition and Tracking XXIX, 1064906.Google Scholar
- Redmon, J. and Farhadi, A. 2018. YOLOv3: An Incremental Improvement, arxiv.Google Scholar
- Ren, S., He, K., Girshick, R., and Sun, J. 2015. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, Advances in Neural Information Processing Systems.Google Scholar
- Stauffer, C. and Grimson, W. E. L. 1999. Adaptive Background Mixture Models for Real-Time Tracking, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2, 2246--252.Google Scholar
- Zhang, J., Xiong, T., Tran, T., Chin, S., and Etienne-Cummings, R. 2016. Compact All-CMOS Spatio-Temporal Compressive Sensing Video Camera with Pixel-Wise Coded Exposure, Optics Express, 24(8), 9013--9024.Google ScholarCross Ref
- Zhao, Z., Chen, H., Chen, G., Kwan, C., and Li, X. R. 2006. Comparison of Several Ballistic Target Tracking Filters, Proc. American Control Conference, 2197--2202.Google Scholar
- Zhao, Z., Chen, H., Chen, G., Kwan, C., and Li, X. R. 2006. IMM-LMMSE Filtering Algorithm for Ballistic Target Tracking with Unknown Ballistic Coefficient, Proc. SPIE, Volume 6236, Signal and Data Processing of Small Targets.Google Scholar
- Zhou, J. and Kwan C. 2018. Anomaly Detection in Low Quality Traffic Monitoring Videos Using Optical Flow, Proc. SPIE 10649, Pattern Recognition and Tracking XXIX.Google Scholar
- Zhou, J. and Kwan, C. 2018. Tracking of Multiple Pixel Targets Using Multiple Cameras, 15th International Symposium on Neural Networks.Google Scholar
Index Terms
- Object Tracking and Classification in Videos Using Compressive Measurements
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