Veliki nadzorni sustav: detekcija i praćenje sumnjivih obrazaca pokreta u prometnim gužvama

The worldwide increasing sentiment of insecurity gave birth to a new era, shaking thereby the intelligent video-surveillance systems design and deployment. The large-scale use of these means has prompted the creation of new needs in terms of analysis and interpretation. For this purpose, behavior recognition and scene understanding related applications have become more captivating to a significant number of computer vision researchers, particularly when crowded scenes are concerned. So far, motion analysis and tracking remain challenging due to significant visual ambiguities, which encourage looking into further keys. By this work, we present a new framework to recognize various motion patterns, extract abnormal behaviors and track them over a multi-camera traffic surveillance system. We apply a density-based technique to cluster motion vectors produced by optical flow, and compare them with motion pattern models defined earlier. Non-identified clusters are treated as suspicious and simultaneously tracked over an overlapping camera network for as long as possible. To aiming the network configuration, we designed an active camera scheduling strategy where camera assignment was realized via an improved Weighted Round-Robin algorithm. To validate our approach, experiment results are presented and discussed.Širom svijeta rasprostranjeni osjećaj nesigurnosti postavio je temelje za dizajniranje i implementaciju inteligentnih sustava nadzora. Velika upotreba ovih sredstava potaknula je stvaranje novih potreba analize i interpretacije. U ovu svrhu, prepoznavanje ponašanja i razumijevanje prizora postaju sve privlačnije povezane primjene značajnom broju istraživača računalne vizije, posebno kada se radi o vrlo prometnim prizorima. Analiza pokreta i slijeđenja ostalo je izazovno područje zbog značajnih vizualnih nejasnoća koje zahtijevaju daljnja istraživanja. U radu je prikazan novi okvir za prepoznavanje različitih uzoraka pokreta, izoliranje neprirodnih ponašanja i njihovo praćenje pomoću nadzornog sustava prometa s više kamera. Primjenjuje se na gustoći zasnovana tehnika skupa vektora pokreta sastavljenih iz optičkog toka te uspoređenih s ranije definiranim modelima uzoraka. Neidentificirani skupovi tretiraju se kao sumnjivi i istovremeno su praćeni mrežom s više preklapajućih kamera što je duže moguće. S ciljem konfiguriranja mreže, dizajnirana je strategija raspoređivanja aktivnih kamera gdje je dodjela kamere ostvarena pomoću unaprijeđenog "Weighted Round-Robin" algoritma

Ag2PdP2O7

Disilver(I) palladium(II) diphosphate, Ag2PdP2O7, is isotypic with Na2PdP2O7. It consists of infinite diphosphato-pallad­ate(II) [Pd(P2O7)2/2]2− ribbons with the PdII ion in an almost square-planar coordination ( symmetry) and the P2O7 group exhibiting 2 symmetry. The [Pd(P2O7)2/2]2− ribbons are linked by distorted [AgO6] octa­hedra. 31P-MAS NMR studies on Ag2PdP2O7 are in accordance with one independent site for phospho­rus; its isotropic chemical shift δiso = 21.5 p.p.m. is similar to that of Pd2P2O7

Lithium cobalt(II) pyrophosphate, Li1.86CoP2O7, from synchrotron X-ray powder data

Structure refinement of high-resolution X-ray powder diffraction data of the title compound gave the composition Li1.865CoP2O7, which is also verified by the ICP measurement. Two Co sites exist in the structure: one is a CoO5 square pyramid and the other is a CoO6 octa­hedron. They share edges and are further inter­connected through P2O7 groups, forming a three-dimensional framework, which exhibits different kinds of inter­secting tunnels containing Li cations and could be of great inter­est in Li ion battery chemistry. The structure also exhibits cation disorder with 13.5% Co residing at the lithium (Li1) site. Co seems to have an average oxidation state of 2.135, as obtained from the strutural stochiometry that closely supports the magnetic susceptibility findings