Who is who at different cameras: people re-identification using depth cameras

This study proposes the concept of bodyprints to perform re-identification of people in surveillance videos. Bodyprints are obtained using calibrated depth-colour cameras such as kinect. The author's results on a database of 40 people show that bodyprints are very robust to changes of pose, point of view and illumination. Potential applications include tracking people with networks of non-overlapping cameras. © 2012 The Institution of Engineering and Technology.The work presented in this paper has been funded by the Spanish Ministry of Science and Technology under the CICYT contract TEVISMART, TEC2009-09146.Albiol Colomer, AJ.; Albiol Colomer, A.; Oliver Moll, J.; Mossi García, JM. (2012). Who is who at different cameras: people re-identification using depth cameras. IET Computer Vision. 6(5):378-387. https://doi.org/10.1049/iet-cvi.2011.0140S37838765Dee, H. M., & Velastin, S. A. (2007). How close are we to solving the problem of automated visual surveillance? Machine Vision and Applications, 19(5-6), 329-343. doi:10.1007/s00138-007-0077-zhttp://www.pointclouds.org/Zhang, Z., & Troje, N. F. (2005). View-independent person identification from human gait. Neurocomputing, 69(1-3), 250-256. doi:10.1016/j.neucom.2005.06.002Bazzani, L., Cristani, M., Perina, A., Farenzena, M., & Murino, V. (2010). Multiple-Shot Person Re-identification by HPE Signature. 2010 20th International Conference on Pattern Recognition. doi:10.1109/icpr.2010.349Doretto, G., Sebastian, T., Tu, P., & Rittscher, J. (2011). Appearance-based person reidentification in camera networks: problem overview and current approaches. Journal of Ambient Intelligence and Humanized Computing, 2(2), 127-151. doi:10.1007/s12652-010-0034-yBk, S., Corvee, E., Bremond, F., & Thonnat, M. (2010). Person Re-identification Using Spatial Covariance Regions of Human Body Parts. 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance. doi:10.1109/avss.2010.34Da-Jinn Wang, Chao-Ho Chen, Tsong-Yi Chen, & Chien-Tsung Lee. (2009). People Recognition for Entering & Leaving a Video Surveillance Area. 2009 Fourth International Conference on Innovative Computing, Information and Control (ICICIC). doi:10.1109/icicic.2009.293Bird, N. D., Masoud, O., Papanikolopoulos, N. P., & Isaacs, A. (2005). Detection of Loitering Individuals in Public Transportation Areas. IEEE Transactions on Intelligent Transportation Systems, 6(2), 167-177. doi:10.1109/tits.2005.848370Oliveira, I. O. de, & Pio, J. L. de S. (2009). People Reidentification in a Camera Network. 2009 Eighth IEEE International Conference on Dependable, Autonomic and Secure Computing. doi:10.1109/dasc.2009.33Hamdoun, O., Moutarde, F., Stanciulescu, B., & Steux, B. (2008). Person re-identification in multi-camera system by signature based on interest point descriptors collected on short video sequences. 2008 Second ACM/IEEE International Conference on Distributed Smart Cameras. doi:10.1109/icdsc.2008.4635689Office, U.H.: ‘i-LIDS multiple camera tracking scenario definition’, 2008)http://www.gpiv.upv.es/kinect_data/http://www.primesense.com/http://www.openni.org/http://opencv.willowgarage.com/http://www.ros.org/http://kinectforwindows.org/Grimaud, M. (1992). New measure of contrast: the dynamics. Image Algebra and Morphological Image Processing III. doi:10.1117/12.60650Beucher, S., and Meyer, F.: ‘The morphological approach to segmentation: the watershed transformation’, (Marcel-Dekker 1992), p. 433–4

Yield-limiting macronutrients for rice in sub-Saharan Africa.

Nutrient deficiencies are considered major constraints to rice production in sub-Saharan Africa (SSA), but a systematic, continent-wide analysis of these constraints is lacking. The objective of this study was to assess responses of rice yields to macronutrients (N, P, and K) across major rice production systems [irrigated lowland rice (IL), rainfed lowland rice (RL), and rainfed upland rice (RU)] and diverse agro-ecological zones (semi-arid, sub-humid, humid, and highlands) in SSA. Diagnostic trials were conducted in 17 countries in 18–60 farmers' fields in each of 30 sites (totaling 1037 farmers' fields), of which 12 sites were in IL, 15 in RL and 3 in RU. All trials had an NPK treatment, and three treatments in which either nitrogen (N), phosphorus (P), or potassium (K) was omitted from the NPK treatment (minus N, P, and K plots). The trials were conducted during one or two seasons. Regression between yields in the NPK and minus N plots was used for calculating N fertilizer requirement, following the approach used for the development of a nutrient management decision support tool for rice (RiceAdvice) in SSA. Site mean yields in the NPK treatment ranged from 1.4 to 7.3 t ha−1. On average, these yields were 1.1 t ha−1 higher than farmers' yields previously observed in the same sites. N was generally the most limiting nutrient, followed by P. Rice yields without N, P, and K were 68, 84, and 89% of yields in the NPK treatment, and were positively correlated with those in the NPK treatment irrespective of production systems and agro-ecological zone (AEZ). Results from a regression model across sites indicated that achieving 3, 5, and 7 t ha−1 requires N fertilizer application at an average rate of 56, 91, and 122 kg N ha−1, respectively. However, there was significant treatment by site interaction on rice yield. Site mean yields were significantly lower without N, P, and K in 93, 60, and 50% of sites as compared to the NPK treatment. Yield response to macronutrients varied across sites and was not related to production system, nor AEZ. Results from this study will help further improvement of RiceAdvice for providing decision support tailored to the particular site, zone, or production system, thereby closing yield gaps, improving fertilizer nutrient efficiency and preventing negative environmental consequences of fertilizer use