Deep Learning-Based Multiple Object Visual Tracking on Embedded System for IoT and Mobile Edge Computing Applications

Compute and memory demands of state-of-the-art deep learning methods are still a shortcoming that must be addressed to make them useful at IoT end-nodes. In particular, recent results depict a hopeful prospect for image processing using Convolutional Neural Netwoks, CNNs, but the gap between software and hardware implementations is already considerable for IoT and mobile edge computing applications due to their high power consumption. This proposal performs low-power and real time deep learning-based multiple object visual tracking implemented on an NVIDIA Jetson TX2 development kit. It includes a camera and wireless connection capability and it is battery powered for mobile and outdoor applications. A collection of representative sequences captured with the on-board camera, dETRUSC video dataset, is used to exemplify the performance of the proposed algorithm and to facilitate benchmarking. The results in terms of power consumption and frame rate demonstrate the feasibility of deep learning algorithms on embedded platforms although more effort to joint algorithm and hardware design of CNNs is needed.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Early Paleogene climate at mid latitude in South America: mineralogical and paleobotanical proxies from continental sequences in Golfo San Jorge basin (Patagonia, Argentina)

The Paleocene-Eocene boundary was a period of transient and intense global warming that had a deep effect on middle and high latitude plant groups. Nevertheless, only scarce early Paleogene paleoclimatic records are known from the South American continental sequences deposited at these latitudes. In this contribution clay mineralogy and paleobotanical analyses (fossil woods and phytoliths) were used as paleoclimate proxies from the lower and middle parts of the Río Chico Group (Golfo San Jorge basin, Patagonia, Argentina). These new data may enable to understand the changing climatic conditions during part of the Paleocene-Eocene transition. In this setting, three clay mineral assemblages were identified: S1 assemblage (smectite) dominates the Peñas Coloradas Formation; S2 assemblage (smectite>kaolinite) occurs in the stratigraphic transition to the Las Flores Formation; and S3 assemblage (kaolinite>smectite) dominates the Las Flores Formation. These trend of change in the detrital clay mineral composition is interpreted as resulting mainly from the changing paleoclimatic conditions that shifted from seasonal warm temperate to tropical affecting the same source area lithology. Moreover, the paleobotanical data suggest that the Early Paleogene vegetation in the Golfo San Jorge basin underwent significant composition and diversity changes, ranging from mixed temperate - subtropical forest to mixed subtropical - tropical, humid forest. The integrated analysis of the clay mineral composition and the palaeobotanical assemblages suggests that, in central Argentinean Patagonia, the Paleocene-Eocene climate changed from temperate warm, humid and highly seasonal precipitation conditions to subtropical-tropical, more continuous year-round rainfall conditions

Real-Time Siamese Multiple Object Tracker with Enhanced Proposals

Maintaining the identity of multiple objects in real-time video is a challenging task, as it is not always feasible to run a detector on every frame. Thus, motion estimation systems are often employed, which either do not scale well with the number of targets or produce features with limited semantic information. To solve the aforementioned problems and allow the tracking of dozens of arbitrary objects in real-time, we propose SiamMOTION. SiamMOTION includes a novel proposal engine that produces quality features through an attention mechanism and a region-of-interest extractor fed by an inertia module and powered by a feature pyramid network. Finally, the extracted tensors enter a comparison head that efficiently matches pairs of exemplars and search areas, generating quality predictions via a pairwise depthwise region proposal network and a multi-object penalization module. SiamMOTION has been validated on five public benchmarks, achieving leading performance against current state-of-the-art trackers. Code available at: https://github.com/lorenzovaquero/SiamMOTIONComment: Accepted at Pattern Recognition. Code available at https://github.com/lorenzovaquero/SiamMOTIO

Spatio-temporal object detection from UAV on board cameras

We propose a new two stage spatio-temporal object detector framework able to improve detection precision by taking into account temporal information. First, a short-term proposal linking and aggregation method improves box features. Then, we design a long-term attention module that further enhances short-term aggregated features adding long-term spatio-temporal information. This module takes into account object trajectories to effectively exploit long-term relationships between proposals in arbitrary distant frames. Many videos recorded from UAV on board cameras have a high density of small objects, making the detection problem very challenging. Our method takes advantage of spatiotemporal information to address these issues increasing the detection robustness. We have compared our method with state-of-the-art video object detectors in two different publicly available datasets focused on UAV recorded videos. Our approach outperforms previous methods in both datasets.This research was partially funded by the Spanish Ministry of Science, Innovation and Universities under grants TIN2017-84796-C2-1-R and RTI2018-097088-B-C32, and the Galician Ministry of Education, Culture and Universities under grants ED431C 2018/29, ED431C 2017/69 and accreditation 2016-2019, ED431G/08. These grants are co-funded by the European Regional Development Fund (ERDF/FEDER program)

CMOS-3D smart imager architectures for feature detection

This paper reports a multi-layered smart image sensor architecture for feature extraction based on detection of interest points. The architecture is conceived for 3-D integrated circuit technologies consisting of two layers (tiers) plus memory. The top tier includes sensing and processing circuitry aimed to perform Gaussian filtering and generate Gaussian pyramids in fully concurrent way. The circuitry in this tier operates in mixed-signal domain. It embeds in-pixel correlated double sampling, a switched-capacitor network for Gaussian pyramid generation, analog memories and a comparator for in-pixel analog-to-digital conversion. This tier can be further split into two for improved resolution; one containing the sensors and another containing a capacitor per sensor plus the mixed-signal processing circuitry. Regarding the bottom tier, it embeds digital circuitry entitled for the calculation of Harris, Hessian, and difference-of-Gaussian detectors. The overall system can hence be configured by the user to detect interest points by using the algorithm out of these three better suited to practical applications. The paper describes the different kind of algorithms featured and the circuitry employed at top and bottom tiers. The Gaussian pyramid is implemented with a switched-capacitor network in less than 50 μs, outperforming more conventional solutions.Xunta de Galicia 10PXIB206037PRMinisterio de Ciencia e Innovación TEC2009-12686, IPT-2011-1625-430000Office of Naval Research N00014111031

Cultural Democracy, Cultural Equity, and Cultural Policy : Perspectives from the UK and USA

Peer reviewedPublisher PD

Dati archeologici e analisi archeometriche di vasetti tipo “San Martino” rinvenuti in Emilia

Analisi archeometriche petrografiche, mineralogiche e chimiche di ceramiche neolitiche tipo "San Martino" da Vicofertile, Collecchio, Parma via Guido Rossi, Gaione, Razza di Campeine. Le analisi indicano una produzione locale nei diversi siti con materie prime fini e cottura intorno agli 800 gradi

Paleoecology and paleoenvironments of Podocarp trees in the Ameghino Petrified forest (Golfo San Jorge Basin, Patagonia, Argentina): constraints for Early Paleogene paleoclimate

During the Early Paleocene (Danian), Central Patagonia had a warm-temperate climate and was dominated by evergreen coniferous forests. Abundant permineralized conifer woods along with some dicot and palm leaf compressions were found in the Ameghino Petrified Forest, and provide evidence of this type of flora. All the permineralized wood and large trunks recovered were assigned to the species Podocarpoxylon mazzonii. An estimated tree height of 17-29m was calculated on the basis of diameter measurements. Based on 14 ring sequences, with a total of 169 rings, the mean ring width and Mean Sensitivity (MS) were 1.23 and 0.19mm respectively. The growth rings are moderately wide, extremely uniform and complacent, indicating that the environment was favourable and constant, and lacked significant stress factors limiting tree growth. Following the quantitative analysis for conifers outlined by Falcon-Lang, the growth ring anatomy of the Podocarpoxylon mazzonii suggests that these trees had an evergreen habit. The combination of the fossil flora, growth ring, and sedimentological analyses suggest that this mostly evergreen coniferous forest developed under warm-temperate conditions and without limiting factors