A Short-Term Biometric Based System for Accurate Personalized Tracking
Surveillance systems have long been in the focus of the research community. Although the accurate detection of the human presence in the scene is now possible even under extreme environmental conditions via the advanced modern camera sensors, efficient personalized tracking is still an open issue and a significant challenge for researchers addressing. Moreover, personalized tracking will not only enhance the tracking robustness but it can also find useful application in several commercial surveillance use-cases, ranging from security to occupancy statistics (i.e. per building, per space and per human). In this respect, this paper introduces a novel the biometric approach for enhanced privacy preserving human tracking based on a novel soft-biometric feature of humans. The moving blobs in the recorded scene can be easily detected in the colour images, while the human silhouettes are detected from the corresponding depth ones. The state-of-the-art 3D Weighted Walkthroughs (3DWW) transformation is applied on the extracted human 3D point cloud, forming thus, a short-term soft biometric signature. The re-authentication of the humans is performed via the comparison of their last valid signature with current one. A thorough analysis on the adjustment of the system’s optimal operational settings has been carried out and the experimental results illustrate the promising robustness, accuracy and efficiency on human tracking performance.
KeywordsMotion detection Human tracking Surveillance Geometric identification
This work is co-funded by the European Union (EU) within the SMILE project under grant agreement number 740931. The SMILE project is part of the EU Framework Program for Research and Innovation Horizon 2020.
- 3.Beymer, D.: Person counting using stereo. In: Workshop on Human Motion, pp. 127–133 (2000)Google Scholar
- 4.Black, J., Ellis, T., Rosin, P.: Multi-view image surveillance and tracking. In: IEEE Workshop on Motion and Video Computing (2002)Google Scholar
- 7.Focken, D., Stiefelhagen, R.: Towards vision-based 3D people tracking in a smart room. In: IEEE International Conference on Multimodal Interfaces (2002)Google Scholar
- 10.Xu, X., Tang, J., Liu, X., Zhang, X.: Human behavior understanding for video surveillance: recent advance. In: 2010 IEEE International Conference in Systems Man and Cybernetics (SMC), pp. 3867–3873 (2010)Google Scholar
- 11.Jia, X., Lu, H., Yang, M.: Visual tracking via adaptive structural local sparse appearance model. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1822–1829 (2012)Google Scholar
- 12.Kang, J., Cohen, I., Medioni, G.: Tracking people in crowded scenes across multiple cameras. In: Proceedings of Asian Conference on Computer Vision (2004)Google Scholar
- 13.Mikic, I., Santini, S., Jain, R.: Video processing and integration from multiple cameras. In: Image Understanding Workshop (1998)Google Scholar
- 15.Otsuka, K., Mukawa, N.: Multi-view occlusion analysis for tracking densely populated objects based on 2D visual angles. In: Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR) (2004)Google Scholar
- 16.Salih, Y., Malik, A.: 3D tracking using particle filters. In: Proceedings of IEEE Instrumentation and Measurement Technology Conference (I2MTC), pp. 1–4 (2011)Google Scholar
- 17.Yang, D., Gonzales-Banos, H., Guibas, L.: Counting people in crowds with a real-time network of simple image sensors. In: International Conference on Computer Vision, pp. 122–129 (2003)Google Scholar
- 18.Krinidis, S., Stavropoulos, G., Ioannidis, D., Tzovaras, D.: A robust and real-time multi-space occupancy extraction system exploiting privacy-preserving sensors. In: International Symposium on Communications, Control and Signal Processing (2014)Google Scholar
- 19.De Silva, L.: Audiovisual sensing of human movements for home-care and security in a smart environment. Int. J. Smart Sens. Intell. Syst. 1, 220–245 (2008)Google Scholar