The re-identification problem has been commonly accomplished using appearance features based on salient points and color information. In this paper, we focus on the possibilities that simple geometric features obtained from depth images captured with RGB-D cameras may offer for the task, particularly working under severe illumination conditions. The results achieved for different sets of simple geometric features extracted in a top-view setup seem to provide useful descriptors for the re-identification task, which can be integrated in an ambient intelligent environment as part of a sensor network.
References
[1]
Bravo, J.; Villarreal, V.; Hervás, R.; Urzaiz, G. Using a communication model to collect measurement data through mobile devices. Sensors 2012, 12, 9253–9272.
[2]
Valero, E.; Adan, A.; Cerrada, C. Automatic construction of 3D basic-semantic models of inhabited interiors using laser scanners and RFID sensors. Sensors 2012, 12, 5705–5724.
[3]
Dollár, P.; Wojek, C.; Schiele, B.; Perona, P. Pedestrian detection: An evaluation of the state of the art. IEEE Trans. Pattern Anal. Mach. Intell. 2012, 34, 743–761.
[4]
Cheng, D.; Cristani, M.; Stoppa, M.; Bazzani, L.; Murino, V. Custom Pictorial Structures for Re-identification. Proceedings of the British Machine Vision Conference, Scotland, UK, 29 August–2 September 2011.
[5]
D'Angelo, A.; Dugelay, J.L. People re-identification in camera networks based on probabilistic color histograms. Proc. SPIE 2011, doi:10.1117/12.876453.
[6]
Bazzani, L.; Cristani, M.; Murino, V. Symmetry-driven accumulation of local features for human characterization and re-identification. Comput. Vision Image Underst. 2013, 117, 130–144.
[7]
Lo Presti, L.; Sclaroff, S.; La Cascia, M. Object Matching in Distributed Video Surveillance Systems by LDA-Based Appearance Descriptors. Proceedings of the ICIAP, Vietri sul Mare, Italy, 8–11 September 2009.
[8]
Mun？oz Salinas, R.; Aguirre, E.; García-Silvente, M. People detection and tracking using stereo vision and color. Image Vision Comput. 2007, 25, 995–1007.
[9]
Everingham, M.; Sivic, J.; Zisserman, A. Taking the bite out of automated naming of characters in TV video. Image Vision Comput. 2009, 27, 545559.
[10]
Jarudi, I.; Sinha, P. Relative Roles of Internal and External Features in Face Recognition. Technical Report memo 225; CBCL, 2005.
[11]
Khoshelham, K.; Elberink, S.O. Accuracy and resolution of kinect depth data for indoor mapping applications. Sensors 2012, 12, 1437–1454.
[12]
Xia, L.; Chen, C.C.; Aggarwal, J.K. Human Detection Using Depth Information by Kinect. Proceedings of the International Workshop on Human Activity Understanding from 3D Data in Conjunction with CVPR (HAU3D), Colorado Springs, CO, USA, 20-25 June 2011.
[13]
Shotton, J.; Fitzgibbon, A.; Cook, M.; Sharp, T.; Finocchio, M.; Moore, R.; Kipma, A.; Blake, A. Real-Time Human Pose Recognition in Parts from a Single Depth Image. Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition, Colorado Springs, CO, USA, 20-25 June 2011.
[14]
Harville, M. Stereo person tracking with adaptive plan-view templates of height and occupancy statistics. Image Vision Comput. 2004, 22, 127–142.
[15]
Albiol, A.; Albiol, A.; Oliver, J.; Mossi, J. Who is who at different cameras: People re-identification using depth cameras. IET Comput. Vision 2011, 6, 378–387.
[16]
Barbosa, B.I.; Cristani, M.; Bue, A.D.; Bazzani, L.; Murino, V. Re-identification with RGB-D sensors. Lect. Note. Comput. Sci. 2012, 7583, 433–442.
[17]
Oliver, J.; Albiol, A.; Albiol, A. 3D Descriptor for People Re-Identification. Proceedings of the 21st International Conference on Pattern Recognition (ICPR), Tsukuba, Japan, 11-15 November 2012.
[18]
Satta, R.; Pala, F.; Fumera, G.; Roli, F. Real-time Appearance-based Person Re-identification over Multiple Kinect Cameras. Proceedings of the 8th International Conference on Computer Vision Theory and Applications (VISAPP), Barcelona, Spain, 21-24 February 2013.
[19]
Yahiaoui, T.; Khoudour, L.; Meurie, C. Real-time passenger counting in buses using dense stereovision. J. Electron. Imag. 2010, doi:10.1117/1.3455989.
[20]
Englebienne, G.; van Oosterhout, T.; Krose, B. Tracking in Sparse Multi-Camera Setups Using Stereo Vision. Proceedings of the Third ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC), Como, Italy, 30 August-2 September 2009.
[21]
Lorenzo-Navarro, J.; Castrillón-Santana, M.; Hernández-Sosa, D. An study on re-identification in RGB-D imagery. Lect. Note. Comput. Sci. 2012, 7657, 200–207.
[22]
Zivkovic, Z.; der Heijden, F. Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern Recognit. Lett. 2006, 27, 773–780.
[23]
Stauffer, G. Adaptive Background Mixture Models for Real-time Tracking. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Fort Collins, CO, USA, 23-25 June 1999; pp. 246–252.
[24]
Andriluka, M.; Roth, S.; Schiele, B. People-Tracking-by-Detection and People-Detection-by-Tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA, 23-28 June 2008.
[25]
Leibe, B.; Schindler, K.; Cornelis, N.; Gool, L.J. V Coupled object detection and tracking from static cameras and moving vehicles. IEEE Trans. Pattern Anal. Mach. Intell. 2008, 30, 1683–1698.
