Factors influencing farmers' forestland-use changes over 15 years (2005–2020) in Thua Thien Hue province, Vietnam

Over the last decades, Vietnam has seen substantial shifts in forest landscape uses and associated livelihoods. We document the livelihood transformations in Nam Dong, a mountainous district of Central Vietnam, where land uses have changed from the utilisation of products from natural forests and shifting cultivation (swidden agriculture) to acacia tree-dominated plantation forestry. Forestry policies (forestland allocation, plantation development agendas), the increase in the economic value of acacia, and household livelihood assets are the primary factors driving these changes. We also found that there are differences in the access to and ownership of forestland with regard to households of different communities and between poor vs wealthy households. Therefore, careful attention needs to be paid to guide future land use policies in the area to foster social and ecological sustainability. HIGHLIGHTS • Major livelihood and forestland-use changes have taken place in central Vietnam over the last two decades. • There has been widespread conversion of forestland (degraded natural forests, swidden land) and cropland to acacia plantations. • Household-scale forestland use changes were primarily driven by forestry policies, the market for woodchips, and land resource access. • There is inequality in access to and ownership of forestland between poor and wealthier households in the mountain district of Vietnam. • Cases of illegal forestland conversions pose challenges to ensuring sustainable forest landscapes

RIMOC, a feature to discriminate unstructured motions: Application to violence detection for video-surveillance

International audienceIn video-surveillance, violent event detection is of utmost interest. Although action recognition has been well studied in computer vision, literature for violence detection in video is far sparser, and even more for surveillance applications. As aggressive events are difficult to define due to their variability and often need high-level interpretation, we decided to first try to characterize what is frequently present in video with violent human behaviors, at a low level: jerky and unstructured motion. Thus, a novel problem-specific Rotation-Invariant feature modeling MOtion Coherence (RIMOC) was proposed, in order to capture its structure and discriminate the unstructured motions. It is based on the eigenvalues obtained from the second-order statistics of the Histograms of Optical Flow vectors from consecutive temporal instants, locally and densely computed, and further embedded into a spheric Riemannian manifold. The proposed RIMOC feature is used to learn statistical models of normal coherent motions in a weakly supervised manner. A multi-scale scheme applied on an inference-based method allows the events with erratic motion to be detected in space and time, as good candidates of aggressive events. We experimentally show that the proposed method produces results comparable to a state-of-the-art supervised approach, with added simplicity in training and computation. Thanks to the compactness of the feature, real-time computation is achieved in learning as well as in detection phase. Extensive experimental tests on more than 18 h of video are provided in different in-lab and real contexts, such as railway cars equipped with on-board cameras

Crowd Behavior Analysis Using Local Mid-Level Visual Descriptors

International audienceCrowd behavior analysis has recently emerged as an increasingly important and dedicated problem for crowd monitoring and management in the visual surveillance community. In particular, it is receiving a lot of attention to detect potentially dangerous situations and to prevent overcrowdedness. In this paper, we propose to quantify crowd properties by a rich set of visual descriptors. The calculation of these descriptors is realized through a novel spatio-temporal model of the crowd. It consists of modeling time-varying dynamics of the crowd using local feature tracks. It also involves a Delaunay triangulation to approximate neighborhood interactions. In total, the crowd is represented as an evolving graph, where the nodes correspond to the tracklets. From this graph, various mid-level representations are extracted to determine the ongoing crowd behaviors. In particular, the effectiveness of the proposed visual descriptors is demonstrated within three applications: crowd video classification, anomaly detection, and violence detection in crowds. The obtained results on videos from different data sets prove the relevance of these visual descriptors to crowd behavior analysis. In addition, by means of comparisons to other existing methods, we demonstrate that the proposed descriptors outperform the state-of-the-art methods with a significant margin using the most challenging data sets

Fast and accurate video annotation using dense motion hypotheses

International audienceBuilding large video datasets is a crucial task for many applications but is also very expensive in practice. In order to avoid annotating all the frames, the annotations from the labeled frames can be propagated using an offline tracker for each object. Following methods based on dynamic programming and eventually distance transforms, we introduce a new penalization which favors some given displacements between two frames without increasing the complexity of the optimization. In order to speed up this step we also propose to use an exact coarse to fine process. Experimental results show that the proposed energy performs better than previous ones and that our exact coarse to fine optimization leads to a significant speed-up in some scenarios

Bidirectional sparse representations for multi-shot person re-identification

Conference of 13th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2016 ; Conference Date: 23 August 2016 Through 26 August 2016; Conference Code:124803International audienceWith the development of surveillance cameras, person re-identification has gained much interest, however re-identifying people across cameras remains a challenging problem which not only requires a good feature description but also a reliable matching scheme. Our method can be applied with any feature and focuses on the second requirement. We propose a robust bidirectional sparse coding method that improves simple sparse coding performances. Some recent work have already explored sparse representation for the re-identification task but none has considered the problem from both the probe and the gallery perspectives. We propose a bidirectional sparse representations method which searches for the most likely match for the test element in the gallery set and makes sure that the selected gallery match is indeed closely related to the probe. Extensive experiments on two datasets, CUHK03 and iLIDS-VID, show the effectiveness of our approach

An Unsupervised Learning Based Aprroach for Unexpected Event Detection

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Tracking of Objects of Interest in a Sequence of Images

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Measuring ground subsidence in Ha Noi through the radar interferometry technique using terrasar-x and cosmos skymed data

International audienceMultitemporal synthetic aperture radar (SAR) interferometry (InSAR) is a widely used technique to measure the ground subsidence and has already shown its ability to map such phenomena on a large spatial scale with millimetric accuracy from space. In Vietnam, to have independent SAR data for surface risk applications, a new X-band SAR mission (JV-LOTUSat) has been scheduled for launch for the 2019-2020 timeframe. However, Vietnam is located in tropical regions where their conditions are impacted by strong atmosphere. The aim of this article is to provide a better understanding of the capabilities of the X-band for estimating the ground subsidence under tropical atmospheric conditions. Analysis is carried out on two stacks, TerraSAR-X and Cosmos SkyMed X-band, from 2011 to 2014 in Ha Noi. We show that the results on the ground subsidence from InSAR processing can describe consistently the subsidence area based on ground measurements. This article demonstrates that the InSAR technique can be effective at detecting and estimating the subsidence phenomena even with the X-band and under conditions typical of tropical regions. The displacement results from TerraSAR-X and Cosmos SkyMed datasets are consistent, with a correlation coefficient (R2) of 0.91 for the period during which their coverage overlaps. Groundwater overexploitation is one of the main causes of the ground subsidence in Ha Noi. This study provides strong support for the scientific potential of the X-band SAR space-borne mission in Vietnam and other tropical countries because it demonstrates the feasibility of the ground subsidence estimates by the X-band SAR, even in conditions impacted by strong atmosphere