1,721,029 research outputs found
Hierarchal decomposition for unusual fish trajectory detection
No full textFish behavior analysis is presented using an unusual trajectory detection method. The proposed method is based on a hierarchy which is formed using the similarity of clustered and labeled data applying hierarchal data decomposition. The fish trajectories from unconstrained underwater videos are classified as normal and unusual where normal trajectories represents common behaviors of fish and unusual trajectories represent rare behaviors. A new trajectory is classified using the constructed hierarchy where different heuristics are applicable. The main contribution of the proposed method is presenting a novel supervised approach to unusual behavior detection (where many methods in this field are unsupervised) which demonstrates significantly improved results
Setting the stage for the machine intelligence era in marine science
Full text linkMachine learning, a subfield of artificial intelligence, offers various methods that can be applied in marine science. It supports data-driven learning, which can result in automated decision making of de novo data. It has significant advantages compared with manual analyses that are labour intensive and require considerable time. Machine learning approaches have great potential to improve the quality and extent of marine research by identifying latent patterns and hidden trends, particularly in large datasets that are intractable using other approaches. New sensor technology supports collection of large amounts of data from the marine environment. The rapidly developing machine learning subfield known as deep learning-which applies algorithms (artificial neural networks) inspired by the structure and function of the brain-is able to solve very complex problems by processing big datasets in a short time, sometimes achieving better performance than human experts. Given the opportunities that machine learning can provide, its integration into marine science and marine resource management is inevitable. The purpose of this themed set of articles is to provide as wide a selection as possible of case studies that demonstrate the applications, utility, and promise of machine learning in marine science. We also provide a forward-look by envisioning a marine science of the future into which machine learning has been fully incorporated
Adaptive mean-shift for automated multi object tracking
No full textMean-shift tracking plays an important role in computer vision applications because of its robustness, ease of implementation and computational efficiency. In this study, a fully automatic multiple-object tracker based on mean-shift algorithm is presented. Foreground is extracted using a mixture of Gaussian followed by shadow and noise removal to initialise the object trackers and also used as a kernel mask to make the system more efficient by decreasing the search area and the number of iterations to converge for the new location of the object. By using foreground detection, new objects entering to the field of view and objects that are leaving the scene could be detected. Trackers are automatically refreshed to solve the potential problems that may occur because of the changes in objects' size, shape, to handle occlusion-split between the tracked objects and to detect newly emerging objects as well as objects that leave the scene. Using a shadow removal method increases the tracking accuracy. As a result, a method that remedies problems of mean-shift tracking and presents an easy to implement, robust and efficient tracking method that can be used for automated static camera video surveillance applications is proposed. Additionally, it is shown that the proposed method is superior to the standard mean-shift
A filtering mechanism for normal fish trajectories
No full textUnderstanding fish behavior by extracting normal motion patterns and then identifying abnormal behaviors is important for understanding the effects of environmental change. In the literature, there are many studies on normal/abnormal behavior detection in the areas of human behaviour analysis, traffic surveillance, and nursing home surveillance, etc. However, the literature is very limited in terms of normal/abnormal fish behavior understanding especially when natural habitat applications are considered. In this study, we present a rule based trajectory filtering mechanism to extract normal fish trajectories which potentially helps to increase the accuracy of the abnormal fish behavior detection systems and can be used as a preliminary method especially when the number of abnormal fish behaviors are very small (e.g. 40-50 times smaller) compared to the number of normal fish behaviors and/or when the number of trajectories are huge
A multimodal approach for individual tracking of people and their belongings
No full textIn this study, a fully automatic surveillance system for indoor environments which is capable of tracking multiple objects using both visible and thermal band images is proposed. These two modalities are fused to track people and the objects they carry separately using their heat signatures and the owners of the belongings are determined. Fusion of complementary information from different modalities (for example, thermal images are not affected by shadows and there is no thermal reflection or halo effect in visible images) is shown to result in better object detection performance. We use adaptive background modeling and local intensity operation for object detection and the mean-shift tracking algorithm for fully automatic tracking. Trackers are refreshed to resolve potential problems which may occur due to the changes in object's size, shape and to handle occlusion-split and to detect newly emerging objects as well as objects that leave the scene. The proposed scheme is applied to the abandoned object detection problem and the results are compared with the state of art methods. The results show that the proposed method facilitate individual tracking of objects for various applications, and provide lower false alarm rates compared to the state of art methods when applied to the abandoned object detection problem
Classifying imbalanced data sets using similarity based hierarchical decomposition
Full text linkAbstract Classification of data is difficult if the data is imbalanced and classes are overlapping. In recent years, more research has started to focus on classification of imbalanced data since real world data is often skewed. Traditional methods are more successful with classifying the class that has the most samples (majority class) compared to the other classes (minority classes). For the classification of imbalanced data sets, different methods are available, although each has some advantages and shortcomings. In this study, we propose a new hierarchical decomposition method for imbalanced data sets which is different from previously proposed solutions to the class imbalance problem. Additionally, it does not require any data pre-processing step as many other solutions need. The new method is based on clustering and outlier detection. The hierarchy is constructed using the similarity of labeled data subsets at each level of the hierarchy with different levels being built by different data and feature subsets. Clustering is used to partition the data while outlier detection is utilized to detect minority class samples. The comparison of the proposed method with state of art the methods using 20 public imbalanced data sets and 181 synthetic data sets showed that the proposed method׳s classification performance is better than the state of art methods. It is especially successful if the minority class is sparser than the majority class. It has accurate performance even when classes have sub-varieties and minority and majority classes are overlapping. Moreover, its performance is also good when the class imbalance ratio is low, i.e. classes are more imbalanced
Detection of abandoned objects using thermal and visible band tracking Görünür ve termal bant taki̇bi̇ kullanilarak terk edi̇lmi̇ş nesne tespi̇ti̇
No full textBu çalışmada, insanlar ve taşıdıkları nesnelerin ayrı takibini temel alan bir terk edilmiş nesne tespiti yöntemi sunulmaktadır. İnsanların ve taşıdıkları nesnelerin ayrı takibini sağlayabilmek için görünür bant verisine ek olarak termal bant veri kullanılmış ve bu nesneler iyileştirilmiş, uyarlamalı ortalama kayma takibi ile takip edilmişlerdir. Termal ve görünür bant verilerin tümleştirilmesi ve sıcaklık bilgisinden yararlanılarak nesneler; insan ve taşınan nesne şeklinde sınıflandırılır, bu nesnelerin gezingeleri bulunur, taşınan nesnelerin sahipleri belirlenir ve terk edilmiş şüpheli nesneler tespit edilerek alarm verilir. Ortalama kayma takibine ek olarak, uyarlanabilir arka plan modellemesi ve yerel yeğinlik operasyonu kullanılmış ve tamamen otomatik bir takip sistemi oluşturulmuştur. Sonuçlara göre; önerilen yöntem dayanıklı, düşük yanlış alarm oranı ile diğer yöntemlerle karşılaştırılabilir ve gözetleme operatörlerine yardımcı olmak amacıyla halka açık iç alan uygulamalarında kullanılabilir bir yöntemdir
Affect Recognition in Hand-Object Interaction Using Object-Sensed Tactile and Kinematic Data
No full textWe investigate the recognition of the affective states of a person performing an action with an object, by processing the object-sensed data. We focus on sequences of basic actions such as grasping and rotating, which are constituents of daily-life interactions. iCube, a 5 cm cube, was used to collect tactile and kinematics data that consist of tactile maps (without information on the pressure applied to the surface), and rotations. We conduct two studies: classification of i) emotions and ii) the vitality forms. In both, the participants perform a semi-structured task composed of basic actions. For emotion recognition, 237 trials by 11 participants associated with anger, sadness, excitement, and gratitude were used to train models using 10 hand-crafted features. The classifier accuracy reaches up to 82.7%. Interestingly, the same classifier when learned exclusively with the tactile data performs on par with its counterpart modeled with all 10 features. For the second study, 1135 trials by 10 participants were used to classify two vitality forms. The best-performing model differentiated gentle actions from rude ones with an accuracy of 84.85%. The results also confirm that people touch objects differently when performing these basic actions with different affective states and attitudes
Automatic Recognition of Commensal Activities in Co-located and Online settings
No full textTechnological advancement has profoundly impacted how people share meals, fostering research interest in new forms of commensality such as tele-dining and eating with artificial companions. Consequently, there is a need to develop computational methods for recognizing commensal activities, that is, actions related to food consumption and social signals displayed during meal-time. This paper introduces the first dataset that consists of synchronized video data of co-located dining dyads. The dataset is annotated with key social signals such as speaking activity, smiling, and food-related activities like chewing and food intake. Unlike previous studies that use remote settings, this work emphasizes the differences between online and co-located setups. A set of machine learning experiments is conducted on our and existing datasets, reaching the best F-score of 0.82. The cross-dataset analysis between co-located and online datasets also evidences the significant disparity between these two settings. While mixing co-located and online recordings may increase the model's generalizability, we notice strong differences between the two settings, highlighting the importance of in-person data recordings for accurate recognition
Detecting abnormal fish trajectories using clustered and labeled data
Full text linkWe propose an approach for the analysis of fish trajectories in unconstrained underwater videos. Trajectories are classified into two classes: normal trajectories which contain the usual behavior of fish and abnormal trajectories which indicate the behaviors that are not as common as the normal class. The paper presents two innovations: 1) a novel approach to abnormal trajectory detection and 2) improved performance on video based abnormal trajectory analysis of fish in unconstrained conditions. First we extract a set of features from trajectories and apply PCA. We then perform clustering on a subset of features. Based on the clustering, outlier detection is applied to each cluster. Improved results are obtained which is significant considering the challenges of underwater environments, low video quality, and erratic movement of fish
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