Human detection from aerial imagery for automatic counting of shellfish gatherers

International audienceAutomatic human identification from aerial image time series or video sequences is a challenging issue. We propose here a complete processing chain that operates in the context of recreational shellfish gatherers counting in a coastal environment (the Gulf of Morbihan, South Brittany, France). It starts from a series of aerial photographs and builds a mosaic in order to prevent multiple occurrences of the same objects on the overlapping parts of aerial images. To do so, several stitching techniques are reviewed and discussed in the context of large aerial scenes. Then people detection is addressed through a sliding window analysis combining the HOG descriptor and a supervised classifier. Several classification methods are compared, including SVM, Random Forests, and AdaBoost. Experimental results show the interest of the proposed approach, and provides directions for future research

Extraction non supervisée de descripteurs pour des suivis environnementaux aériens

International audienc

Human detection from aerial imagery for automatic counting of shellfish gatherers

International audienceAutomatic human identification from aerial image time series or video sequences is a challenging issue. We propose here a complete processing chain that operates in the context of recreational shellfish gatherers counting in a coastal environment (the Gulf of Morbihan, South Brittany, France). It starts from a series of aerial photographs and builds a mosaic in order to prevent multiple occurrences of the same objects on the overlapping parts of aerial images. To do so, several stitching techniques are reviewed and discussed in the context of large aerial scenes. Then people detection is addressed through a sliding window analysis combining the HOG descriptor and a supervised classifier. Several classification methods are compared, including SVM, Random Forests, and AdaBoost. Experimental results show the interest of the proposed approach, and provides directions for future research

Active detection for aerial survey

International audienc

Data annotation with active learning: application to environmental surveys

International audienceAn active learning framework is introduced to deal with reducing the annotation cost for aerial images in environmental surveys. The selection of the queried instances at each step of the active process is here constrained by requiring that they belong to a group, an image (or a part of it) in our case. A score to rank the images and identify the one that should be annotated at each iteration is defined, based on both classifier uncertainty and performances. The performances of several strategies regarding the interaction gain are discussed based on an experiment on real image data collected for an environmental survey.Une procédure d'apprentissage actif est proposée pour réduire le coût d'annotation d'images aériennes pour des suivis environnementaux. La sélection des instances à étiqueter a chaque étape du processus actif est contrainte à l'appartenance à un groupe, une image (ou une partie d'image) dans notre cas. Un score pour classer les images et identifier celle qui doit être annotée à chaque itération est défini, en fonction de l'incertitude et des performances de détection du classifieur. Les performances de plusieurs stratégies concernant le gain d'interaction avec l'utilisateur sont discutées à partir d'une expérience sur des données d'images réelles collectées pour une étude environnementale

Apprentissage actif pour l'annotation d'images aériennes appliqué aux suivis environnementaux

International audiencepas de résum

Apprentissage actif pour l'annotation d'images aériennes appliqué aux suivis environnementaux

National audiencepas de résum

Data annotation with active learning: application to environmental surveys

International audienceAn active learning framework is introduced to deal with reducing the annotation cost for aerial images in environmental surveys. The selection of the queried instances at each step of the active process is here constrained by requiring that they belong to a group, an image (or a part of it) in our case. A score to rank the images and identify the one that should be annotated at each iteration is defined, based on both classifier uncertainty and performances. The performances of several strategies regarding the interaction gain are discussed based on an experiment on real image data collected for an environmental survey.Une procédure d'apprentissage actif est proposée pour réduire le coût d'annotation d'images aériennes pour des suivis environnementaux. La sélection des instances à étiqueter a chaque étape du processus actif est contrainte à l'appartenance à un groupe, une image (ou une partie d'image) dans notre cas. Un score pour classer les images et identifier celle qui doit être annotée à chaque itération est défini, en fonction de l'incertitude et des performances de détection du classifieur. Les performances de plusieurs stratégies concernant le gain d'interaction avec l'utilisateur sont discutées à partir d'une expérience sur des données d'images réelles collectées pour une étude environnementale

Active Learning to Assist Annotation of Aerial Images in Environmental Surveys

International audienceNowadays, remote sensing technologies greatly ease environmental assessment using aerial images. Such data are most often analyzed by a manual operator, leading to costly and non scalable solutions. In the fields of both machine learning and image processing, many algorithms have been developed to fasten and automate this complex task. Their main common assumption is the need to have prior ground truth available. However, for field experts or engineers, manually labeling the objects requires a time-consuming and tedious process. Restating the labeling issue as a binary classification one, we propose a method to assist the costly annotation task by introducing an active learning process, considering a query-by-group strategy. Assuming that a comprehensive context may be required to assist the annotator with the labeling task of a single instance, the labels of all the instances of an image are indeed queried. A score based on instances distribution is defined to rank the images for annotation and an appropriate retraining step is derived to simultaneously reduce the interaction cost and improve the classifier performances at each iteration. A numerical study on real images is conducted to assess the algorithm performances. It highlights promising results regarding the classification rate along with the chosen retraining strategy and the number of interactions with the user

Active Learning to Assist Annotation of Aerial Images in Environmental Surveys

International audienceNowadays, remote sensing technologies greatly ease environmental assessment using aerial images. Such data are most often analyzed by a manual operator, leading to costly and non scalable solutions. In the fields of both machine learning and image processing, many algorithms have been developed to fasten and automate this complex task. Their main common assumption is the need to have prior ground truth available. However, for field experts or engineers, manually labeling the objects requires a time-consuming and tedious process. Restating the labeling issue as a binary classification one, we propose a method to assist the costly annotation task by introducing an active learning process, considering a query-by-group strategy. Assuming that a comprehensive context may be required to assist the annotator with the labeling task of a single instance, the labels of all the instances of an image are indeed queried. A score based on instances distribution is defined to rank the images for annotation and an appropriate retraining step is derived to simultaneously reduce the interaction cost and improve the classifier performances at each iteration. A numerical study on real images is conducted to assess the algorithm performances. It highlights promising results regarding the classification rate along with the chosen retraining strategy and the number of interactions with the user