PixelTrack: A Fast Adaptive Algorithm for Tracking Non-rigid Objects

International audienceIn this paper, we present a novel algorithm for fast tracking of generic objects in videos. The algorithm uses two components: a detector that makes use of the generalised Hough transform with pixel-based descriptors, and a probabilistic segmentation method based on global models for foreground and background. These components are used for tracking in a combined way, and they adapt each other in a co-training manner. Through effective model adaptation and segmentation, the algorithm is able to track objects that undergo rigid and non-rigid deformations and considerable shape and appearance variations. The proposed tracking method has been thoroughly evaluated on challenging standard videos, and outperforms state-of-theart tracking methods designed for the same task. Finally, the proposed models allow for an extremely efficient implementation, and thus tracking is very fast

Whole-brain radiomics for clustered federated personalization in brain tumor segmentation

Federated learning and its application to medical image segmentation have recently become a popular research topic. This training paradigm suffers from statistical heterogeneity between participating institutions' local datasets, incurring convergence slowdown as well as potential accuracy loss compared to classical training. To mitigate this effect, federated personalization emerged as the federated optimization of one model per institution. We propose a novel personalization algorithm tailored to the feature shift induced by the usage of different scanners and acquisition parameters by different institutions. This method is the first to account for both inter and intra-institution feature shift (multiple scanners used in a single institution). It is based on the computation, within each centre, of a series of radiomic features capturing the global texture of each 3D image volume, followed by a clustering analysis pooling all feature vectors transferred from the local institutions to the central server. Each computed clustered decentralized dataset (potentially including data from different institutions) then serves to finetune a global model obtained through classical federated learning. We validate our approach on the Federated Brain Tumor Segmentation 2022 Challenge dataset (FeTS2022). Our code is available at (https://github.com/MatthisManthe/radiomics_CFFL).Comment: Accepted at Medical Imaging with Deep Learning (MiDL) 2023 conferenc

Coherent Selection of Independent Trackers for Real-time Object Tracking

International audienceThis paper presents a new method for combining several independent and heterogeneous tracking algorithms for the task of online single-object tracking. The proposed algorithm runs several trackers in parallel, where each of them relies on a different set of complementary low-level features. Only one tracker is selected at a given frame, and the choice is based on a spatio-temporal coherence criterion and normalised confidence estimates. The key idea is that the individual trackers are kept completely independent, which reduces the risk of drift in situations where for example a tracker with an inaccurate or inappropriate appearance model negatively impacts the performance of the others. Moreover, the proposed approach is able to switch between different tracking methods when the scene conditions or the object appearance rapidly change. We experimentally show with a set of Online Adaboost-based trackers that this formulation of multiple trackers improves the tracking results in comparison to more classical combinations of trackers. And we further improve the overall performance and computational efficiency by introducing a selective update step in the tracking framework

Personalized Posture and Fall Classification with Shallow Gated Recurrent Units

Link to final publication : https://ieeexplore.ieee.org/document/8787455International audienceActivities of Daily Living (ADL) classification is a key part of assisted living systems as it can be used to assess a person autonomy. We present in this paper an activity classification pipeline using Gated Recurrent Units (GRU) and inertial sequences. We aim to take advantage of the feature extraction properties of neural networks to free ourselves from defining rules or manually choosing features. We also investigate the advantages of resampling input sequences and personalizing GRU models to improve the performances. We evaluate our models on two datasets: a dataset containing five common postures: sitting, lying, standing, walking and transfer and a dataset named MobiAct V2 providing ADL and falls. Results show that the proposed approach could benefit eHealth services and particularly activity monitoring

Long-Range Transformer Architectures for Document Understanding

Since their release, Transformers have revolutionized many fields from Natural Language Understanding to Computer Vision. Document Understanding (DU) was not left behind with first Transformer based models for DU dating from late 2019. However, the computational complexity of the self-attention operation limits their capabilities to small sequences. In this paper we explore multiple strategies to apply Transformer based models to long multi-page documents. We introduce 2 new multi-modal (text + layout) long-range models for DU. They are based on efficient implementations of Transformers for long sequences. Long-range models can process whole documents at once effectively and are less impaired by the document's length. We compare them to LayoutLM, a classical Transformer adapted for DU and pre-trained on millions of documents. We further propose 2D relative attention bias to guide self-attention towards relevant tokens without harming model efficiency. We observe improvements on multi-page business documents on Information Retrieval for a small performance cost on smaller sequences. Relative 2D attention revealed to be effective on dense text for both normal and long-range models.Comment: Conference: ICDAR 2023 Workshops on Document Analysis and Recognitio

Are We Ready for Self-Quantifying and Preventive Health Behavior at Work? Exploring Employees’ Types and Engagement

The boundaries between life and work become blurred, and new work patterns are very demanding for employees. Future work environments should consider employees’ health and pay more attention to digital interventions for preventive health and well-being at work. Accordingly, this study focuses on identifying employees’ needs and triggers to engage in self-quantifying at work. To assess this objective, we develop employees’ types based on survey data and cluster analysis. Our empirical results emphasize that the open-minded improvers are willing to engage and that they are not susceptible at all, while the conscious pragmatists value the perceived usefulness and autonomy of self-quantifying at work. The vigilant hesitaters might be triggered by social comparison. Our research provides a new perspective on engagement in self-quantifying, and insights for preventive health behavior, healthy employees, and well-being in future work environments. These results offer starting points for meaningful work to stay employable and productive

Segmentation of vectorial image features using shape gradients and information measures

International audienceIn this paper, we propose to focus on the segmentation of vectorial features (e.g. vector fields or color intensity) using region-based active contours. We search for a domain that minimizes a criterion based on homogeneity measures of the vectorial features. We choose to evaluate, within each region to be segmented, the average quantity of information carried out by the vectorial features, namely the joint entropy of vector components. We do not make any assumption on the underlying distribution of joint probability density functions of vector components, and so we evaluate the entropy using non parametric probability density functions. A local shape minimizer is then obtained through the evolution of a deformable domain in the direction of the shape gradient. The first contribution of this paper lies in the methodological approach used to differentiate such a criterion. This approach is mainly based on shape optimization tools. The second one is the extension of this method to vectorial data. We apply this segmentation method on color images for the segmentation of color homogeneous regions. We then focus on the segmentation of synthetic vector fields and show interesting results where motion vector fields may be separated using both their length and their direction. Then, optical flow is estimated in real video sequences and segmented using the proposed technique. This leads to promising results for the segmentation of moving video objects

Leveraging Colour Segmentation for Upper-Body Detection

This paper presents an upper-body detection algorithm that extends classical shape-based detectors through the use of additional semantic colour segmentation cues. More precisely, candidate upper-body image patches produced by a base detector are soft-segmented using a multi-class probabilistic colour segmentation algorithm that leverages spatial as well as colour prior distributions for different semantic object regions (skin, hair, clothing, background). These multi-class soft segmentation maps are then classified as true or false upper-bodies. By further fusing the score of this latter classifier with the base detection score, the method shows a performance improvement on three different public datasets and using two different upper-body base detectors, demonstrating the complementarity of the contextual semantic colour segmentation and the base detector

The TA2 Database – A Multi-Modal Database From Home Entertainment

This paper presents a new database containing high-definition audio and video recordings in a rather unconstrained video-conferencing-like environment. The database consists of recordings of people sitting around a table in two separate rooms communicating and playing online games with each other. Extensive annotation of head positions, voice activity and word transcription has been performed on the dataset, making it especially useful for evaluating automatic speech-recognition, voice activity detection, speaker localisation, multi-face detection and tracking, and other audio-visual analysis algorithms

Classifying Global Scene Context for On-line Multiple Tracker Selection

International audienceIn this paper, we present a novel framework for combining several independent on-line trackers using visual scene context. The aim of our method is to decide automatically at each point in time which specific tracking algorithm works best under the given scene or acquisition conditions. To this end, we define a set of generic global context features computed on each frame of a set of training videos. At the same time, we record the performance of each individual tracker on these videos in terms of object bounding box overlap with the ground truth. Then a classifier is trained to estimate which tracker gives the best result given the global scene context in a particular frame. We experimentally show that such a classifier can predict the best tracker with a precision of over 80% in unknown videos with unknown environments. The proposed tracking method further filters the classifier responses temporarily using a Hidden Markov Model in order to avoid rapid oscillations between different trackers. Finally, we evaluated the overall tracking system and showed that this scene context-based tracker selection considerably improves the overall robustness and compares favourably with the state-of-the-art