Despite over a decade of legislative efforts to address modern slavery in the supply chains of large corporations, the effectiveness of government oversight remains hampered by the challenge of scrutinizing thousands of statements annually. While Large Language Models (LLMs) can be considered a well established solution for the automatic analysis and summarization of documents, recognizing concrete modern slavery countermeasures taken by companies and differentiating those from vague claims remains a challenging task. To help evaluate and fine-tune LLMs for the assessment of corporate statements, we introduce a dataset composed of 5,731 modern slavery statements taken from the Australian Modern Slavery Register and annotated at the sentence level. This paper details the construction steps for the dataset that include the careful design of annotation specifications, the selection and preprocessing of statements, and the creation of high-quality annotation subsets for effective model evaluations. To demonstrate our dataset's utility, we propose a machine learning methodology for the detection of sentences relevant to mandatory reporting requirements set by the Australian Modern Slavery Act. We then follow this methodology to benchmark modern language models under zero-shot and supervised learning settings.

With the increasing development of advanced unmanned aerial vehicles (UAVs), communication between operators and these intelligent systems is becoming more stressful. For the safety of UAV flights, automatic psychological stress detection is becoming a key research topic for successful missions. Stress can be reliably estimated via some biological markers which are not appropriate in many cases of human-machine-interaction setups. In this article, we propose a non-intrusive deep learning-based stress level estimation approach. The goal is to identify the region where the operator's emotional state projects in the space defined by the latent dimensional emotions of arousal and valence since the stress region is well delimited in this space. The proposed multimodal approach uses sequential temporal CNN and LSTM with an Attention Weighted Average layer in the vision modality. As a second modality, we investigate local and global descriptors such as Mel-frequency cepstral coefficients, i-vector embeddings as well as Fisher-vector encodings. The multimodal-fusion approach uses a strategy referred to as “late-fusion” that involves the combination of unimodal model outputs as inputs of the decision engine. Since we have to deal with more naturalistic behavior in operator-machine interaction contexts, the One minute Gradual Emotion Challenge dataset was used for predictive model validation.

Earth Observations (EO) enable scientific research, such as the study of meteorology and climate, ecosystems and forests, hydrology and marine life. Applications of EO help protect populations from disasters and improve life in intelligent cities. Increasingly, Machine Learning techniques are seen as key to solve these complex multidisciplinary problems. The scale and dimensionality of data involved often require the definition of processing chains, or workflows. Standards can facilitate the composition, sharing, execution and discovery of these workflows and applications, making them more useful. This paper presents three applications based on Deep Learning: a tree species classifier, a car detector and a flood detector. These applications rely on software containers to package ML framework and algorithms, as well as on workflows to process EO data. We found that these practices allow improved reuse and deployment of research assets in infrastructures. We also note the strong discriminative capabilities of Deep Learning on smaller datasets and the difficulty of gen-eralization to other methods of sensing or regions of interest.

Background subtraction is often used as the first step in video analysis and smart surveillance applications. However, the issue of inconsistent performance across different scenarios due to a lack of flexibility remains a serious concern. To address this, we propose a novel non-parametric, pixel-level background modeling approach based on word dictionaries that draws from traditional codebooks and sample consensus approaches. In this new approach, the importance of each background sample (or word) is evaluated online based on their recurrence among all local observations. This helps build smaller pixel models that are better suited for long-term foreground detection. Combining these models with a frame-level dictionary and local feedback mechanisms leads us to our proposed background subtraction method, coined “PAWCS.” Experiments on the 2012 and 2014 versions of the ChangeDetection.net data set show that PAWCS outperforms 26 previously tested and published methods in terms of overall F-Measure as well as in most categories taken individually. Our results can be reproduced with a C++ implementation available online.

Tracking with a Pan-Tilt-Zoom (PTZ) camera has been a research topic in computer vision for many years. However, it is difficult to assess the progress that has been made because there is no standard evaluation methodology. The difficulty in evaluating PTZ tracking algorithms arises from their dynamic nature. In contrast to other forms of tracking, PTZ tracking involves both locating the target in the image and controlling the motors of the camera to aim it so that the target stays in its field of view. This type of tracking can only be performed online. In this paper, we propose a new evaluation framework based on a virtual PTZ camera. With this framework, tracking scenarios do not change for each experiment and we are able to replicate the main principles of online PTZ camera control and behavior including camera positioning delays, tracker processing delays, and numerical zoom. We tested our evaluation framework with the Camshift tracker to show its viability and to establish baseline results.

Although there has long been interest in foreground background segmentation based on change detection for video surveillance applications, the issue of inconsistent performance across different scenarios remains a serious concern. To address this, we propose a new type of word based approach that regulates its own internal parameters using feedback mechanisms to withstand difficult conditions while keeping sensitivity intact in regular situations. Coined "PAWCS", this method's key advantages lie in its highly persistent and robust dictionary model based on color and local binary features as well as its ability to automatically adjust pixel-level segmentation behavior. Experiments using the 2012 Change Detection.net dataset show that it outranks numerous recently proposed solutions in terms of overall performance as well as in each category. A complete C++ implementation based on OpenCV is available online.

Foreground/background segmentation via change detection in video sequences is often used as a stepping stone in high-level analytics and applications. Despite the wide variety of methods that have been proposed for this problem, none has been able to fully address the complex nature of dynamic scenes in real surveillance tasks. In this paper, we present a universal pixel-level segmentation method that relies on spatiotemporal binary features as well as color information to detect changes. This allows camouflaged foreground objects to be detected more easily while most illumination variations are ignored. Besides, instead of using manually set, frame-wide constants to dictate model sensitivity and adaptation speed, we use pixel-level feedback loops to dynamically adjust our method's internal parameters without user intervention. These adjustments are based on the continuous monitoring of model fidelity and local segmentation noise levels. This new approach enables us to outperform all 32 previously tested state-of-the-art methods on the 2012 and 2014 versions of the ChangeDetection.net dataset in terms of overall F-Measure. The use of local binary image descriptors for pixel-level modeling also facilitates high-speed parallel implementations: our own version, which used no low-level or architecture-specific instruction, reached real-time processing speed on a midlevel desktop CPU. A complete C++ implementation based on OpenCV is available online.

Most of the recently published background subtraction methods can still be classified as pixel-based, as most of their analysis is still only done using pixel-by-pixel comparisons. Few others might be regarded as spatial-based (or even spatiotemporal-based) methods, as they take into account the neighborhood of each analyzed pixel. Although the latter types can be viewed as improvements in many cases, most of the methods that have been proposed so far suffer in complexity, processing speed, and/or versatility when compared to their simpler pixel-based counterparts. In this paper, we present an adaptive background subtraction method, derived from the low-cost and highly efficient ViBe method, which uses a spatiotemporal binary similarity descriptor instead of simply relying on pixel intensities as its core component. We then test this method on multiple video sequences and show that by only replacing the core component of a pixel-based method it is possible to dramatically improve its overall performance while keeping memory usage, complexity and speed at acceptable levels for online applications.