Scene-specific crowd counting using synthetic training images

Crowd counting is a computer vision task on which considerable progress has recently been made thanks to convolutional neural networks. However, it remains a challenging task even in scene-specific settings, in real-world application scenarios where no representative images of the target scene are available, not even unlabelled, for training or fine-tuning a crowd counting model. Inspired by previous work in other computer vision tasks, we propose a simple but effective solution for the above application scenario, which consists of automatically building a scene-specific training set of synthetic images. Our solution does not require from end-users any manual annotation effort nor the collection of representative images of the target scene. Extensive experiments on several benchmark data sets show that the proposed solution can improve the effectiveness of existing crowd counting methods

Human-in-the-loop cross-domain person re-identification

Person re-identification is a challenging cross-camera matching problem, which is inherently subject to domain shift. To mitigate it, many solutions have been proposed so far, based on four kinds of approaches: supervised and unsupervised domain adaptation, direct transfer, and domain generalisation; in particular, the first two approaches require target data during system design, respectively labelled and unlabelled. In this work, we consider a very different approach, known as human-in-the-loopHITL), which consists of exploiting user’s feedback on target data processed during system operation to improve re-identification accuracy. Although it seems particularly suited to this application, given the inherent interaction with a human operator, HITL methods have been proposed for person re-identification by only a few works so far, and with a different purpose than addressing domain shift. However, we argue that HITL deserves further consideration in person re-identification, also as a potential alternative solution against domain shift. To substantiate our view, we consider simple HITL implementations which do not require model re-training or fine-tuning: they are based on well-known relevance feedback algorithms for content-based image retrieval, and of novel versions of them we devise specifically for person re-identification. We then conduct an extensive, cross-data set experimental evaluation of our HITL implementations on benchmark data sets, and compare them with a large set of existing methods against domain shift, belonging to the four categories mentioned above. Our results provide evidence that HITL can be as effective as, or even outperform, existing ad hoc solutions against domain shift for person re-identification, even under the simple implementations we consider. We believe that these results can foster further research on HITL in the person re-identification field, where, in our opinion, its potential has not been thoroughly explored so far

Online domain adaptation for person Re-identification with a human in the loop

Supervised deep learning methods have recently achieved remarkable performance in person re-identification. Unsupervised domain adaptation (UDA) approaches have also been proposed for application scenarios where only unlabelled data are available from target camera views. We consider a more challenging scenario when even collecting a suitable amount of representative, unlabelled target data for offline training or fine-tuning is infeasible. In this context we revisit the human-in-the-loop (HITL) approach, which exploits online the operator's feedback on a small amount of target data. We argue that HITL is a kind of online domain adaptation specifically suited to person re-identification. We then reconsider relevance feedback methods for content-based image retrieval that are computationally much cheaper than state-of-the-art HITL methods for person reidentification, and devise a specific feedback protocol for them. Experimental results show that HITL can achieve comparable or better performance than UDA, and is therefore a valid alternative when the lack of unlabelled target data makes UDA infeasible

Efficient parallel computations of flows of arbitrary fluids for all regimes of Reynolds, Mach and Grashof numbers

This paper presents a unified numerical method able to address a wide class of fluid flow problems of engineering interest. Arbitrary fluids are treated specifying totally arbitrary equations of state, either in analytical form or through look-up tables. The most general system of the unsteady Navier\u2013Stokes equations is integrated with a coupled implicit preconditioned method. The method can stand infinite CFL number and shows the efficiency of a quasi-Newton method independent of the multi-block partitioning on parallel machines. Computed test cases ranging from inviscid hydrodynamics, to natural convection loops of liquid metals, and to supersonic gasdynamics, show a solution efficiency independent of the class of fluid flow problem

Concurrent Validity of Physiological Cost Index in Walking over Ground and during Robotic Training in Subacute Stroke Patients

Physiological Cost Index (PCI) has been proposed to assess gait demand. The purpose of the study was to establish whether PCI is a valid indicator in subacute stroke patients of energy cost of walking in different walking conditions, that is, over ground and on the Gait Trainer (GT) with body weight support (BWS). The study tested if correlations exist between PCI and ECW, indicating validity of the measure and, by implication, validity of PCI. Six patients (patient group (PG)) with subacute stroke and 6 healthy age- and size-matched subjects as control group (CG) performed, in a random sequence in different days, walking tests overground and on the GT with 0, 30, and 50% BWS. There was a good to excellent correlation between PCI and ECW in the observed walking conditions: in PG Pearson correlation was 0.919 (p < 0.001); in CG Pearson correlation was 0.852 (p < 0.001). In conclusion, the high significant correlations between PCI and ECW, in all the observed walking conditions, suggest that PCI is a valid outcome measure in subacute stroke patients