Relevant bibliographies by topics / CAMELS dataset

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'CAMELS dataset'

Author: Grafiati
Published: 4 June 2021
Last updated: 3 February 2022

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Journal articles on the topic "CAMELS dataset"

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Chagas, Vinícius B. P., Pedro L. B. Chaffe, Nans Addor, Fernando M. Fan, Ayan S. Fleischmann, Rodrigo C. D. Paiva, and Vinícius A. Siqueira. "CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in Brazil." Earth System Science Data 12, no. 3 (September 8, 2020): 2075–96. http://dx.doi.org/10.5194/essd-12-2075-2020.

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Abstract. We introduce a new catchment dataset for large-sample hydrological studies in Brazil. This dataset encompasses daily time series of observed streamflow from 3679 gauges, as well as meteorological forcing (precipitation, evapotranspiration, and temperature) for 897 selected catchments. It also includes 65 attributes covering a range of topographic, climatic, hydrologic, land cover, geologic, soil, and human intervention variables, as well as data quality indicators. This paper describes how the hydrometeorological time series and attributes were produced, their primary limitations, and their main spatial features. To facilitate comparisons with catchments from other countries, the data follow the same standards as the previous CAMELS (Catchment Attributes and MEteorology for Large-sample Studies) datasets for the United States, Chile, and Great Britain. CAMELS-BR (Brazil) complements the other CAMELS datasets by providing data for hundreds of catchments in the tropics and the Amazon rainforest. Importantly, precipitation and evapotranspiration uncertainties are assessed using several gridded products, and quantitative estimates of water consumption are provided to characterize human impacts on water resources. By extracting and combining data from these different data products and making CAMELS-BR publicly available, we aim to create new opportunities for hydrological research in Brazil and facilitate the inclusion of Brazilian basins in continental to global large-sample studies. We envision that this dataset will enable the community to gain new insights into the drivers of hydrological behavior, better characterize extreme hydroclimatic events, and explore the impacts of climate change and human activities on water resources in Brazil. The CAMELS-BR dataset is freely available at https://doi.org/10.5281/zenodo.3709337 (Chagas et al., 2020).
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Alvarez-Garreton, Camila, Pablo A. Mendoza, Juan Pablo Boisier, Nans Addor, Mauricio Galleguillos, Mauricio Zambrano-Bigiarini, Antonio Lara, et al. "The CAMELS-CL dataset: catchment attributes and meteorology for large sample studies – Chile dataset." Hydrology and Earth System Sciences 22, no. 11 (November 13, 2018): 5817–46. http://dx.doi.org/10.5194/hess-22-5817-2018.

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Abstract. We introduce the first catchment dataset for large sample studies in Chile. This dataset includes 516 catchments; it covers particularly wide latitude (17.8 to 55.0∘ S) and elevation (0 to 6993 m a.s.l.) ranges, and it relies on multiple data sources (including ground data, remote-sensed products and reanalyses) to characterise the hydroclimatic conditions and landscape of a region where in situ measurements are scarce. For each catchment, the dataset provides boundaries, daily streamflow records and basin-averaged daily time series of precipitation (from one national and three global datasets), maximum, minimum and mean temperatures, potential evapotranspiration (PET; from two datasets), and snow water equivalent. We calculated hydro-climatological indices using these time series, and leveraged diverse data sources to extract topographic, geological and land cover features. Relying on publicly available reservoirs and water rights data for the country, we estimated the degree of anthropic intervention within the catchments. To facilitate the use of this dataset and promote common standards in large sample studies, we computed most catchment attributes introduced by Addor et al. (2017) in their Catchment Attributes and MEteorology for Large-sample Studies (CAMELS) dataset, and added several others. We used the dataset presented here (named CAMELS-CL) to characterise regional variations in hydroclimatic conditions over Chile and to explore how basin behaviour is influenced by catchment attributes and water extractions. Further, CAMELS-CL enabled us to analyse biases and uncertainties in basin-wide precipitation and PET. The characterisation of catchment water balances revealed large discrepancies between precipitation products in arid regions and a systematic precipitation underestimation in headwater mountain catchments (high elevations and steep slopes) over humid regions. We evaluated PET products based on ground data and found a fairly good performance of both products in humid regions (r>0.91) and lower correlation (r<0.76) in hyper-arid regions. Further, the satellite-based PET showed a consistent overestimation of observation-based PET. Finally, we explored local anomalies in catchment response by analysing the relationship between hydrological signatures and an attribute characterising the level of anthropic interventions. We showed that larger anthropic interventions are correlated with lower than normal annual flows, runoff ratios, elasticity of runoff with respect to precipitation, and flashiness of runoff, especially in arid catchments. CAMELS-CL provides unprecedented information on catchments in a region largely underrepresented in large sample studies. This effort is part of an international initiative to create multi-national large sample datasets freely available for the community. CAMELS-CL can be visualised from http://camels.cr2.cl and downloaded from https://doi.pangaea.de/10.1594/PANGAEA.894885.
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Fowler, Keirnan J. A., Suwash Chandra Acharya, Nans Addor, Chihchung Chou, and Murray C. Peel. "CAMELS-AUS: hydrometeorological time series and landscape attributes for 222 catchments in Australia." Earth System Science Data 13, no. 8 (August 6, 2021): 3847–67. http://dx.doi.org/10.5194/essd-13-3847-2021.

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Abstract. This paper presents the Australian edition of the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) series of datasets. CAMELS-AUS (Australia) comprises data for 222 unregulated catchments, combining hydrometeorological time series (streamflow and 18 climatic variables) with 134 attributes related to geology, soil, topography, land cover, anthropogenic influence and hydroclimatology. The CAMELS-AUS catchments have been monitored for decades (more than 85 % have streamflow records longer than 40 years) and are relatively free of large-scale changes, such as significant changes in land use. Rating curve uncertainty estimates are provided for most (75 %) of the catchments, and multiple atmospheric datasets are included, offering insights into forcing uncertainty. This dataset allows users globally to freely access catchment data drawn from Australia's unique hydroclimatology, particularly notable for its large interannual variability. Combined with arid catchment data from the CAMELS datasets for the USA and Chile, CAMELS-AUS constitutes an unprecedented resource for the study of arid-zone hydrology. CAMELS-AUS is freely downloadable from https://doi.org/10.1594/PANGAEA.921850 (Fowler et al., 2020a).
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Coxon, Gemma, Nans Addor, John P. Bloomfield, Jim Freer, Matt Fry, Jamie Hannaford, Nicholas J. K. Howden, et al. "CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain." Earth System Science Data 12, no. 4 (October 12, 2020): 2459–83. http://dx.doi.org/10.5194/essd-12-2459-2020.

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Abstract. We present the first large-sample catchment hydrology dataset for Great Britain, CAMELS-GB (Catchment Attributes and MEteorology for Large-sample Studies). CAMELS-GB collates river flows, catchment attributes and catchment boundaries from the UK National River Flow Archive together with a suite of new meteorological time series and catchment attributes. These data are provided for 671 catchments that cover a wide range of climatic, hydrological, landscape, and human management characteristics across Great Britain. Daily time series covering 1970–2015 (a period including several hydrological extreme events) are provided for a range of hydro-meteorological variables including rainfall, potential evapotranspiration, temperature, radiation, humidity, and river flow. A comprehensive set of catchment attributes is quantified including topography, climate, hydrology, land cover, soils, and hydrogeology. Importantly, we also derive human management attributes (including attributes summarising abstractions, returns, and reservoir capacity in each catchment), as well as attributes describing the quality of the flow data including the first set of discharge uncertainty estimates (provided at multiple flow quantiles) for Great Britain. CAMELS-GB (Coxon et al., 2020; available at https://doi.org/10.5285/8344e4f3-d2ea-44f5-8afa-86d2987543a9) is intended for the community as a publicly available, easily accessible dataset to use in a wide range of environmental and modelling analyses.
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Jehn, Florian U., Konrad Bestian, Lutz Breuer, Philipp Kraft, and Tobias Houska. "Using hydrological and climatic catchment clusters to explore drivers of catchment behavior." Hydrology and Earth System Sciences 24, no. 3 (March 5, 2020): 1081–100. http://dx.doi.org/10.5194/hess-24-1081-2020.

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Abstract. The behavior of every catchment is unique. Still, we seek for ways to classify them as this helps to improve hydrological theories. In this study, we use hydrological signatures that were recently identified as those with the highest spatial predictability to cluster 643 catchments from the CAMELS dataset. We describe the resulting clusters concerning their behavior, location and attributes. We then analyze the connections between the resulting clusters and the catchment attributes and relate this to the co-variability of the catchment attributes in the eastern and western US. To explore whether the observed differences result from clustering catchments by either climate or hydrological behavior, we compare the hydrological clusters to climatic ones. We find that for the overall dataset climate is the most important factor for the hydrological behavior. However, depending on the location, either aridity, snow or seasonality has the largest influence. The clusters derived from the hydrological signatures partly follow ecoregions in the US and can be grouped into four main behavior trends. In addition, the clusters show consistent low flow behavior, even though the hydrological signatures used describe high and mean flows only. We can also show that most of the catchments in the CAMELS dataset have a low range of hydrological behaviors, while some more extreme catchments deviate from that trend. In the comparison of climatic and hydrological clusters, we see that the widely used Köppen–Geiger climate classification is not suitable to find hydrologically similar catchments. However, in comparison with novel, hydrologically based continuous climate classifications, some clusters follow the climate classification very directly, while others do not. From those results, we conclude that the signal of the climatic forcing can be found more explicitly in the behavior of some catchments than in others. It remains unclear if this is caused by a higher intra-catchment variability of the climate or a higher influence of other catchment attributes, overlaying the climate signal. Our findings suggest that very different sets of catchment attributes and climate can cause very similar hydrological behavior of catchments – a sort of equifinality of the catchment response.
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Klingler, Christoph, Karsten Schulz, and Mathew Herrnegger. "LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe." Earth System Science Data 13, no. 9 (September 16, 2021): 4529–65. http://dx.doi.org/10.5194/essd-13-4529-2021.

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Abstract. Very large and comprehensive datasets are increasingly used in the field of hydrology. Large-sample studies provide insights into the hydrological cycle that might not be available with small-scale studies. LamaH-CE (LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe, LamaH for short; the geographical extension “-CE” is omitted in the text and the dataset) is a new dataset for large-sample studies and comparative hydrology in Central Europe. It covers the entire upper Danube to the state border of Austria–Slovakia, as well as all other Austrian catchments including their foreign upstream areas. LamaH covers an area of about 170 000 km2 in nine countries, ranging from lowland regions characterized by a continental climate to high alpine zones dominated by snow and ice. Consequently, a wide diversity of properties is present in the individual catchments. We represent this variability in 859 gauged catchments with over 60 catchment attributes, covering topography, climatology, hydrology, land cover, vegetation, soil and geological properties. LamaH further contains a collection of runoff time series as well as meteorological time series. These time series are provided with a daily and hourly resolution. All meteorological and the majority of runoff time series cover a span of over 35 years, which enables long-term analyses with a high temporal resolution. The runoff time series are classified by over 20 attributes including information about human impacts and indicators for data quality and completeness. The structure of LamaH is based on the well-known CAMELS (Catchment Attributes and MEteorology for Large-sample Studies) datasets. In contrast, however, LamaH does not only consider independent basins, covering the full upstream area. Intermediate catchments are covered as well, which allows together with novel attributes the considering of the hydrological network and river topology in applications. We not only describe the basic datasets used and methodology of data preparation but also focus on possible limitations and uncertainties. LamaH contains additionally results of a conceptual hydrological baseline model for checking plausibility of the inputs as well as benchmarking. Potential applications of LamaH are outlined as well, since it is intended to serve as a uniform data basis for further research. LamaH is available at https://doi.org/10.5281/zenodo.4525244 (Klingler et al., 2021).
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Kratzert, Frederik, Daniel Klotz, Guy Shalev, Günter Klambauer, Sepp Hochreiter, and Grey Nearing. "Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets." Hydrology and Earth System Sciences 23, no. 12 (December 17, 2019): 5089–110. http://dx.doi.org/10.5194/hess-23-5089-2019.

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Abstract. Regional rainfall–runoff modeling is an old but still mostly outstanding problem in the hydrological sciences. The problem currently is that traditional hydrological models degrade significantly in performance when calibrated for multiple basins together instead of for a single basin alone. In this paper, we propose a novel, data-driven approach using Long Short-Term Memory networks (LSTMs) and demonstrate that under a “big data” paradigm, this is not necessarily the case. By training a single LSTM model on 531 basins from the CAMELS dataset using meteorological time series data and static catchment attributes, we were able to significantly improve performance compared to a set of several different hydrological benchmark models. Our proposed approach not only significantly outperforms hydrological models that were calibrated regionally, but also achieves better performance than hydrological models that were calibrated for each basin individually. Furthermore, we propose an adaption to the standard LSTM architecture, which we call an Entity-Aware-LSTM (EA-LSTM), that allows for learning catchment similarities as a feature layer in a deep learning model. We show that these learned catchment similarities correspond well to what we would expect from prior hydrological understanding.
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Haq, Wajiha, Syed Hassan Raza, and Muhammad Wasif Malik. "Missed takes towards a pandemic of COVID-19? A systematic literature review of Coronavirus related diseases in Pakistan." Journal of Infection in Developing Countries 14, no. 07 (July 31, 2020): 726–31. http://dx.doi.org/10.3855/jidc.12771.

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Pakistan is also seeing the profound effect of the outbreak of COVID-19, which demands an urgent investigation of literature and further scientific investigation for cure and prevention. This study has employed the systematic approach for searching the literature from the recently compiled database of researches namely COVID-19 Open Research Dataset (CORD-19) and related diseases. The literature on Pakistan has shown the evidence of human-to-human and animal-to-human transmission of viruses, the presence of antibodies of MERS-CoV in camels, and careless attitude towards preventive measures of such respiratory diseases. There is a lot of gap in the literature regarding coronaviruses and their antibodies creating herd immunity for another coronavirus and COVID-19. In particular to Pakistan, and in general, for other developing countries, a weak health-care system coupled with the trembling economy has many implications of COVID-19 which should be carefully thought-out to combat the spread.
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Ayzel, Georgy, and Maik Heistermann. "The effect of calibration data length on the performance of a conceptual hydrological model versus LSTM and GRU: A case study for six basins from the CAMELS dataset." Computers & Geosciences 149 (April 2021): 104708. http://dx.doi.org/10.1016/j.cageo.2021.104708.

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Ardabili, Ahad Khaleghi, Zied Othman Ahmed, and Ali Layth Abbood. "Solving Routing Problem Using Improved Camel Herds Algorithm." International Journal on Perceptive and Cognitive Computing 6, no. 2 (December 14, 2020): 53–59. http://dx.doi.org/10.31436/ijpcc.v6i2.157.

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This paper introduces a new adaptive, distributed routing algorithm based on the Improved Camel Herds Algorithm (CHA). It is an intelligent, multi-agent optimization algorithm that is inspired by the behavior of camels and how they search for food in their desert environment. We examine its ability to solve the routing problem in switched networks: finding the shortest path in the process of transferring data packets between networks. Many meta-heuristic algorithms have been previously proposed to address the routing problem, and this proposed approach is compared with three well-known algorithms (ACO, GA, PSO) on ten graphs (weighted, integer, and not negative) and datasets with various size of nodes (from 10 nodes to 297 nodes). Three performance criteria were used to evaluate the performance of the algorithms (mean relative error, standard deviation, and number of function evaluations). The results proved that the performance of the proposed algorithm is both promising and competitive with other algorithms.
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Dissertations / Theses on the topic "CAMELS dataset"

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Bizzarri, Diletta. "Classification of large-scale catchments data-sets: use of seasonality statistics in the identification of flood typology." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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River flooding is one of the most destructive natural calamities, causing every year serious economic, societal and environmental consequences. It is therefore of vital importance the investigation of the causative mechanisms of past flooding events. The present research, in the framework of large-scale hydrological classification studies, is inspired by the methodology elaborated by Berghuijs et al. 2019. We here propose a multicriteria classification process, widely transferable across locations, based on seasonality statistics for the evaluation of the recurrence of different flood types at the catchment scale, recognising extreme (one-day) precipitation, soil-moisture excess and snowmelt as the main triggering mechanisms. We then discuss the methodology presented in Stein et al. 2019, which also investigates mixed-flood behaviour at the catchment scale, but using a single-event approach, comparing its outputs with ours for the CAMELS dataset, 671 catchments spread across the contiguous US.
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Jenkins, Jacob Luke. "Navigating campus: a geospatial approach to 3-D routing." Thesis, Kansas State University, 2013. http://hdl.handle.net/2097/15638.

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Master of Landscape Architecture
Department of Landscape Architecture/Regional and Community Planning
Howard Hahn
Evolving needs for universities, municipalities, and corporations demand more sustainable and efficient techniques for data management. Geographic Information Systems (GIS) enables decision makers to spatially analyze the built environment to better understand facility usage by running test scenarios to evaluate current efficiencies and identify opportunities for investment. This can only be conducted when data is organized and leveraged across many departments in a collaborative environment. Data organization through GIS encourages interdepartmental collaboration uniting all efforts on a common front. An organized system facilitates a working relationship between the university and the community of Manhattan increasing efficiency, developing sustainable practices, and enhancing the health and safety of Kansas State University and larger community. Efficiency is increased through automation of many current practices such as work requests and routine maintenance. Sustainable practices will be developed by generating self-guided campus tours and identifying area appropriate for bioswales. Lastly, safety will be enhanced throughout campus by increasing emergency response access, determining areas within buildings difficult to reach in emergency situations, and identifying unsafe areas on campus. Evolving needs for universities, municipalities, and corporations demand more sustainable and efficient techniques for data management. Geographic Information Systems (GIS) enables decision makers to spatially analyze the built environment to better understand facility usage by running test scenarios to evaluate current efficiencies and identify opportunities for investment. This can only be conducted when data is organized and leveraged across many departments in a collaborative environment. Data organization through GIS encourages interdepartmental collaboration uniting all efforts on a common front. An organized system facilitates a working relationship between the university and the community of Manhattan increasing efficiency, developing sustainable practices, and enhancing the health and safety of Kansas State University and larger community. Efficiency is increased through automation of many current practices such as work requests and routine maintenance. Sustainable practices will be developed by generating self-guided campus tours and identifying area appropriate for bioswales. Lastly, safety will be enhanced throughout campus by increasing emergency response access, determining areas within buildings difficult to reach in emergency situations, and identifying unsafe areas on campus. Optimizing data management for Kansas State University was conducted in three phases. First, a baseline assessment for facility management at Kansas State University was conducted through discussions with campus departments. Second, case study interviews and research was conducted with leaders in GIS management. Third, practices for geospatial data management were adapted and implemented for Kansas State University: the building of a centralized database, constructing a 3-dimensional routing network, and modeling a virtual campus in 3D.
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Books on the topic "CAMELS dataset"

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Bazen, Jacques. University spin-offs and economic impact on semi-peripheral regions in the Netherlands. Hogeschool Saxion, lectoraat Regio Ontwikkeling, 2020. http://dx.doi.org/10.14261/f58678f3-daa8-4422-aab7c7fcafa8966d.

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In this study, several aspects of Saxion spin-offs have been analysed, the numbers, workplaces, location, migration, gender issues, different economic sectors and survival rates. The main question underlying all these analyses was what the impact of Saxion as university of applied sciences is on the regional economy of the two regions in which it is located. From the literature, the concept of an entrepreneurial ecosystem, as explanatory factor for the observations that in certain regions more graduates or staff members start their own business and that such an ecosystem helps small fledgling businesses to survive and grow is an interesting concept. Unfortunately, the theoretical foundations are still not fully crystallized, therefore measuring the actual influence of such entrepreneurial ecosystems is still a difficult exercise. In this study, Saxion spin-offs from two regions, Twente and the Cleantech Region, have been analysed, and several differences in terms of number of spin-offs, employment, migration patterns and survival rates have been identified. Since the spin-offs are from the same university of applied sciences, with the same policy regarding support of entrepreneurship and both regions are located outside of the economic core regions of the country, it appears as if the strength of the regional context, the regional entrepreneurial ecosystem and the business opportunities it provides is a factor in explaining why there are more spin-offs in Twente (even when controlling for the larger size of the Saxion campus in this region). If one assumes that the strength of the entrepreneurial ecosystem is stronger in Twente (among others because of existing business networks, the availability of a world class research university, the University of Twente and a business support organization like Novel-T), it would explain why spin-offs located in this region on average offer more workplaces, and have a higher survival rate than in the Cleantech Region. Gender differences related to entrepreneurship are present in Saxion spin-offs, female graduates and staff members are much less likely to start a spin-off company than their male counterparts. When females do start, their spin-offs are on average much smaller in terms of workplaces offered. Their businesses have on average an equal survival rate than those started by a male entrepreneur. Findings from the literature on the subject and the numbers found in this study suggest that there is a need for specific programs in Saxion targeting females, to at least think about starting their own business. Also, specific mentoring programs for spin-offs with female entrepreneurs may help to let these businesses grow and increase their regional economic impact. Saxion spin-offs can be found in many different sectors, something understandable given the broad spectrum of study programs in Saxion. Even though most spin-offs remain micro sized businesses, certain economic sectors seem to offer better scalable business models, especially in sectors such as industry, information and communication technology businesses and business support services. The number as well as employment in the more innovative and internationally competitive topsectors is much higher in the region Twente than in the Cleantech Region, possibly another consequence of the – apparently – stronger regional entrepreneurial ecosystem in Twente. An often-stated argument for regional economic development is that investing in spin-off companies will help to create workplaces in the region, since companies are not very likely to move. In this study, the data on migration of spin-offs have been compared with the migration of graduates, based on the HBO-monitor survey. It is not possible to one-on-one compare the two datasets, as the migration of spin-offs is calculated for the first five years of their existence and the HBO-monitor is held around one and a half year after graduation. Still, w
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Book chapters on the topic "CAMELS dataset"

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Bukhari, Syed Saqib, Faisal Shafait, and Thomas M. Breuel. "The IUPR Dataset of Camera-Captured Document Images." In Camera-Based Document Analysis and Recognition, 164–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29364-1_13.

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Nagy, Robert, Anders Dicker, and Klaus Meyer-Wegener. "NEOCR: A Configurable Dataset for Natural Image Text Recognition." In Camera-Based Document Analysis and Recognition, 150–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29364-1_12.

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Kumar, Jayant, Peng Ye, and David Doermann. "A Dataset for Quality Assessment of Camera Captured Document Images." In Camera-Based Document Analysis and Recognition, 113–25. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05167-3_9.

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Zhu, Ziyi, Liangcai Gao, Yibo Li, Yilun Huang, Lin Du, Ning Lu, and Xianfeng Wang. "NTable: A Dataset for Camera-Based Table Detection." In Document Analysis and Recognition – ICDAR 2021, 117–29. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86331-9_8.

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Janoch, Allison, Sergey Karayev, Yangqing Jia, Jonathan T. Barron, Mario Fritz, Kate Saenko, and Trevor Darrell. "A Category-Level 3D Object Dataset: Putting the Kinect to Work." In Consumer Depth Cameras for Computer Vision, 141–65. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4640-7_8.

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Banerjee, Soumya. "Analysis of a Planetary Scale Scientific Collaboration Dataset Reveals Novel Patterns." In First Complex Systems Digital Campus World E-Conference 2015, 85–90. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45901-1_7.

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Denina, Giovanni, Bir Bhanu, Hoang Thanh Nguyen, Chong Ding, Ahmed Kamal, Chinya Ravishankar, Amit Roy-Chowdhury, Allen Ivers, and Brenda Varda. "VideoWeb Dataset for Multi-camera Activities and Non-verbal Communication." In Distributed Video Sensor Networks, 335–47. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-127-1_23.

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Zhu, Zunjie, Feng Xu, Mingzhu Li, Zheng Wang, and Chenggang Yan. "Challenges from Fast Camera Motion and Image Blur: Dataset and Evaluation." In Computer Vision – ECCV 2020 Workshops, 211–27. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-68238-5_16.

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Wang, Zhe, Daeyun Shin, and Charless C. Fowlkes. "Predicting Camera Viewpoint Improves Cross-Dataset Generalization for 3D Human Pose Estimation." In Computer Vision – ECCV 2020 Workshops, 523–40. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66096-3_36.

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Liciotti, Daniele, Marina Paolanti, Emanuele Frontoni, Adriano Mancini, and Primo Zingaretti. "Person Re-identification Dataset with RGB-D Camera in a Top-View Configuration." In Video Analytics. Face and Facial Expression Recognition and Audience Measurement, 1–11. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56687-0_1.

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Conference papers on the topic "CAMELS dataset"

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Watanabe, Tatsuhisa, Tomoharu Nakashima, and Yoshifumi Kusunoki. "Change Detection For Area Surveillance Using A Moving Camera." In 35th ECMS International Conference on Modelling and Simulation. ECMS, 2021. http://dx.doi.org/10.7148/2021-0220.

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This paper tackles change detection for area surveillance with a moving device. None of the existing datasets for change detection meets a surveillance scenario where a camera is mounted on a moving platform and pointed in the direction of moving. Thus, this paper creates a new dataset including several challenging points. For this dataset, this paper employs a composable method and proposes some components. To evaluate the proposed components, some corresponding classic methods were also tested on the dataset. As a result, the proposals outperformed them. Moreover, this paper investigated the relationship between the parameters of the components and their performance.
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Malon, Thierry, Geoffrey Roman-Jimenez, Patrice Guyot, Sylvie Chambon, Vincent Charvillat, Alain Crouzil, André Péninou, Julien Pinquier, Florence Sèdes, and Christine Sénac. "Toulouse campus surveillance dataset." In MMSys '18: 9th ACM Multimedia Systems Conference. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3204949.3208133.

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Liu, Qianhui, Dong Xing, Huajin Tang, De Ma, and Gang Pan. "Event-based Action Recognition Using Motion Information and Spiking Neural Networks." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/240.

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Event-based cameras have attracted increasing attention due to their advantages of biologically inspired paradigm and low power consumption. Since event-based cameras record the visual input as asynchronous discrete events, they are inherently suitable to cooperate with the spiking neural network (SNN). Existing works of SNNs for processing events mainly focus on the task of object recognition. However, events from the event-based camera are triggered by dynamic changes, which makes it an ideal choice to capture actions in the visual scene. Inspired by the dorsal stream in visual cortex, we propose a hierarchical SNN architecture for event-based action recognition using motion information. Motion features are extracted and utilized from events to local and finally to global perception for action recognition. To the best of the authors’ knowledge, it is the first attempt of SNN to apply motion information to event-based action recognition. We evaluate our proposed SNN on three event-based action recognition datasets, including our newly published DailyAction-DVS dataset comprising 12 actions collected under diverse recording conditions. Extensive experimental results show the effectiveness of motion information and our proposed SNN architecture for event-based action recognition.
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Scheerlinck, Cedric, Henri Rebecq, Timo Stoffregen, Nick Barnes, Robert Mahony, and Davide Scaramuzza. "CED: Color Event Camera Dataset." In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2019. http://dx.doi.org/10.1109/cvprw.2019.00215.

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Sharma, Anil. "Intelligent Querying in Camera Networks for Efficient Target Tracking." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/918.

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Visual analytics applications often rely on target tracking across a network of cameras for inference and prediction. A network of cameras generates immense amount of video data and processing it for tracking a target is highly computationally expensive. Related works typically use data association and visual re-identification techniques to match target templates across multiple cameras. In this thesis, I propose to formulate this scheduling problem as a Markov Decision Process (MDP) and present a reinforcement learning based solution to schedule cameras by selecting one where the target is most likely to appear next. The proposed approach can be learned directly from data and doesn't require any information of the camera network topology. NLPR MCT and DukeMTMC datasets are used to show that the proposed policy significantly reduces the number of frames to be processed for tracking and identifies the camera schedule with high accuracy as compared to the related approaches. Finally, I will be formulating an end-to-end pipeline for target tracking that will learn a policy to find the camera schedule and to track the target in the individual camera frames of the schedule.
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Yu, Zhengxu, Zhongming Jin, Long Wei, Jishun Guo, Jianqiang Huang, Deng Cai, Xiaofei He, and Xian-Sheng Hua. "Progressive Transfer Learning for Person Re-identification." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/586.

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Model fine-tuning is a widely used transfer learning approach in person Re-identification (ReID) applications, which fine-tuning a pre-trained feature extraction model into the target scenario instead of training a model from scratch. It is challenging due to the significant variations inside the target scenario, e.g., different camera viewpoint, illumination changes, and occlusion. These variations result in a gap between the distribution of each mini-batch and the distribution of the whole dataset when using mini-batch training. In this paper, we study model fine-tuning from the perspective of the aggregation and utilization of the global information of the dataset when using mini-batch training. Specifically, we introduce a novel network structure called Batch-related Convolutional Cell (BConv-Cell), which progressively collects the global information of the dataset into a latent state and uses this latent state to rectify the extracted feature. Based on BConv-Cells, we further proposed the Progressive Transfer Learning (PTL) method to facilitate the model fine-tuning process by joint training the BConv-Cells and the pre-trained ReID model. Empirical experiments show that our proposal can improve the performance of the ReID model greatly on MSMT17, Market-1501, CUHK03 and DukeMTMC-reID datasets. The code will be released later on at \url{https://github.com/ZJULearning/PTL}
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Zheng, Feng, Xin Miao, and Heng Huang. "Fast Vehicle Identification in Surveillance via Ranked Semantic Sampling Based Embedding." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/514.

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Identifying vehicles across cameras in traffic surveillance is fundamentally important for public safety purposes. However, despite some preliminary work, the rapid vehicle search in large-scale datasets has not been investigated. Moreover, modelling a view-invariant similarity between vehicle images from different views is still highly challenging. To address the problems, in this paper, we propose a Ranked Semantic Sampling (RSS) guided binary embedding method for fast cross-view vehicle Re-IDentification (Re-ID). The search can be conducted by efficiently computing similarities in the projected space. Unlike previous methods using random sampling, we design tree-structured attributes to guide the mini-batch sampling. The ranked pairs of hard samples in the mini-batch can improve the convergence of optimization. By minimizing a novel ranked semantic distance loss defined according to the structure, the learned Hamming distance is view-invariant, which enables cross-view Re-ID. The experimental results demonstrate that RSS outperforms the state-of-the-art approaches and the learned embedding from one dataset can be transferred to achieve the task of vehicle Re-ID on another dataset.
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Li, Wenhui, Yongkang Wong, An-An Liu, Yang Li, Yu-Ting Su, and Mohan Kankanhalli. "Multi-Camera Action Dataset for Cross-Camera Action Recognition Benchmarking." In 2017 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2017. http://dx.doi.org/10.1109/wacv.2017.28.

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Xiong, Shaomin, Haoyu Wu, and Toshiki Hirano. "A New Human Intruder Detection Scheme for Video Surveillance." In ASME 2019 28th Conference on Information Storage and Processing Systems. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/isps2019-7490.

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Abstract The demand for video surveillance has increased rapidly in recent years. Artificial intelligence (AI) algorithms are key enablers for the smart functionalities of a surveillance camera. Typical smart functionalities include human or object detection, tracking and recognition. However, many of the neural network (NN) algorithms for AI require intensive computation. At the endpoint or edge such as a home surveillance camera, the computation power is limited. The intensive computation also causes higher power consumption, which is also problematic for battery powered cameras. In this paper, we introduce a new human detection scheme that requires much less computation while the accuracy is equivalent to other existing algorithms. It obtains datasets and knowledge from a complex NN algorithm at the learning and calibration phase. These datasets are later used to train two cascading lightweight machine leaning algorithms, which will be used for further human detections. It is demonstrated that the proposed scheme can be run by the camera alone and the speed of detection is much faster than other benchmark NN algorithms.
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Abdelhamed, Abdelrahman, Stephen Lin, and Michael S. Brown. "A High-Quality Denoising Dataset for Smartphone Cameras." In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2018. http://dx.doi.org/10.1109/cvpr.2018.00182.

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