Feature Extraction and Object Classification in Video Sequences for Military Surveillance

A detecção e reconhecimento de objectos requer um sistema de aprendizagem que possa identificar automaticamente um grupo de objectos, independentemente dos dados de entrada. Para que este tipo de identificação seja possível, este sistema precisa analisar previamente um grande grupo de dados para que possa memorizar pontos de interesse de diferentes objectos. Esta é a chamada fase de treino e é o primeiro passo em todos os processos de detecção e reconhecimento de machine learning. Embora já existam muitos modelos que realizam a detecção e reconhecimento de um grande grupo de objectos, um dos objetivos deste projeto é especificar esta identificação para um grupo pequeno e especial de objetos. Isto torna-se possível usando transfer learning, que é um processo que usa o conhecimento adquirido, por um desses modelos, na resolução de um problema e aplica-o para solucionar uma questão diferente. Basicamente, tira proveito do resultado do processo de extração de características e utiliza-o para aprender a identificar outro tipo de objetos. A extração de características é um grupo de processos com o objetivo de simplificar grandes grupos de dados, criando pequenos conjuntos de informações não redundantes. Esses pequenos grupos são mais fáceis de controlar, descrevem totalmente o conjunto de dados original e, ao usá-los, os recursos necessários para analisar um grande conjunto de dados são reduzidos. Neste contexto, os dados a serem analisados ​​serão capturados por uma câmara implementada num ponto estacionário ou num veículo. Quando se lida com a captura de informação visual é normal que um grande número de dados seja gerado. Por isso, é importante analisá-lo com eficiência e identificar informações que são relevantes. Esta dissertação é realizada no âmbito militar, uma vez que os objectos a serem automaticamente identificados são tanques, armas, pessoas e veículos (carros e camiões), alcançando, assim, vigilância territorial.Object detection and recognition requires a learning system that can automatically identify a group of objects independently of the input data. To be able to perform this kind of identification, this system needs to previously analyze a large group of data, so it can memorize special features of different objects. This procedure it's called training and it's the first step in all the detection and recognition processes of machine learning. Although there are already many models that perform detection and recognition for a large group of objects, one of the goals of this project is to specify this identification into a small and special group of objects. This will be achieved by using transfer learning, that is a process that uses the knowledge gained by one of these models while solving one problem and applies it to a different one. Basically, it takes advantage of the feature extraction procedure outputs and use them to learn how to identify other kind of objects. Feature extraction is a group of processes with the goal of simplifying big groups of data by creating small sets of non-redundant information. These small groups are more manageable and can fully describe the original data set and, by using them, the resources necessary to analyse a large set of input data are decreased. In this context, the data to be analyzed will be captured by a camera implemented at a stationary point or in a vehicle. When dealing with the capture of visual information, it's normal that a large number of data is generated. So, it's important to analyze it efficiently and achieve relevant information identification. This dissertation focuses in military uses, therefore these operations are going to be used to automatically identify objects in the military field, that is, tanks, guns, people and vehicles (cars and trucks), achieving territorial surveillance

MAMMALS IN PORTUGAL : A data set of terrestrial, volant, and marine mammal occurrences in P ortugal

Mammals are threatened worldwide, with 26% of all species being includedin the IUCN threatened categories. This overall pattern is primarily associatedwith habitat loss or degradation, and human persecution for terrestrial mam-mals, and pollution, open net fishing, climate change, and prey depletion formarine mammals. Mammals play a key role in maintaining ecosystems func-tionality and resilience, and therefore information on their distribution is cru-cial to delineate and support conservation actions. MAMMALS INPORTUGAL is a publicly available data set compiling unpublishedgeoreferenced occurrence records of 92 terrestrial, volant, and marine mam-mals in mainland Portugal and archipelagos of the Azores and Madeira thatincludes 105,026 data entries between 1873 and 2021 (72% of the data occur-ring in 2000 and 2021). The methods used to collect the data were: live obser-vations/captures (43%), sign surveys (35%), camera trapping (16%),bioacoustics surveys (4%) and radiotracking, and inquiries that represent lessthan 1% of the records. The data set includes 13 types of records: (1) burrowsjsoil moundsjtunnel, (2) capture, (3) colony, (4) dead animaljhairjskullsjjaws, (5) genetic confirmation, (6) inquiries, (7) observation of live animal (8),observation in shelters, (9) photo trappingjvideo, (10) predators dietjpelletsjpine cones/nuts, (11) scatjtrackjditch, (12) telemetry and (13) vocalizationjecholocation. The spatial uncertainty of most records ranges between 0 and100 m (76%). Rodentia (n=31,573) has the highest number of records followedby Chiroptera (n=18,857), Carnivora (n=18,594), Lagomorpha (n=17,496),Cetartiodactyla (n=11,568) and Eulipotyphla (n=7008). The data setincludes records of species classified by the IUCN as threatened(e.g.,Oryctolagus cuniculus[n=12,159],Monachus monachus[n=1,512],andLynx pardinus[n=197]). We believe that this data set may stimulate thepublication of other European countries data sets that would certainly contrib-ute to ecology and conservation-related research, and therefore assisting onthe development of more accurate and tailored conservation managementstrategies for each species. There are no copyright restrictions; please cite thisdata paper when the data are used in publications.info:eu-repo/semantics/publishedVersio

Mammals in Portugal: a data set of terrestrial, volant, and marine mammal occurrences in Portugal

Mammals are threatened worldwide, with ~26% of all species being included in the IUCN threatened categories. This overall pattern is primarily associated with habitat loss or degradation, and human persecution for terrestrial mammals, and pollution, open net fishing, climate change, and prey depletion for marine mammals. Mammals play a key role in maintaining ecosystems functionality and resilience, and therefore information on their distribution is crucial to delineate and support conservation actions. MAMMALS IN PORTUGAL is a publicly available data set compiling unpublished georeferenced occurrence records of 92 terrestrial, volant, and marine mammals in mainland Portugal and archipelagos of the Azores and Madeira that includes 105,026 data entries between 1873 and 2021 (72% of the data occurring in 2000 and 2021). The methods used to collect the data were: live observations/captures (43%), sign surveys (35%), camera trapping (16%), bioacoustics surveys (4%) and radiotracking, and inquiries that represent less than 1% of the records. The data set includes 13 types of records: (1) burrows | soil mounds | tunnel, (2) capture, (3) colony, (4) dead animal | hair | skulls | jaws, (5) genetic confirmation, (6) inquiries, (7) observation of live animal (8), observation in shelters, (9) photo trapping | video, (10) predators diet | pellets | pine cones/nuts, (11) scat | track | ditch, (12) telemetry and (13) vocalization | echolocation. The spatial uncertainty of most records ranges between 0 and 100 m (76%). Rodentia (n =31,573) has the highest number of records followed by Chiroptera (n = 18,857), Carnivora (n = 18,594), Lagomorpha (n = 17,496), Cetartiodactyla (n = 11,568) and Eulipotyphla (n = 7008). The data set includes records of species classified by the IUCN as threatened (e.g., Oryctolagus cuniculus [n = 12,159], Monachus monachus [n = 1,512], and Lynx pardinus [n = 197]). We believe that this data set may stimulate the publication of other European countries data sets that would certainly contribute to ecology and conservation-related research, and therefore assisting on the development of more accurate and tailored conservation management strategies for each species. There are no copyright restrictions; please cite this data paper when the data are used in publications