Generative Adversarial Models for People Attribute Recognition in Surveillance

In this paper we propose a deep architecture for detecting people attributes (e.g. gender, race, clothing ...) in surveillance contexts. Our proposal explicitly deal with poor resolution and occlusion issues that often occur in surveillance footages by enhancing the images by means of Deep Convolutional Generative Adversarial Networks (DCGAN). Experiments show that by combining both our Generative Reconstruction and Deep Attribute Classification Network we can effectively extract attributes even when resolution is poor and in presence of strong occlusions up to 80% of the whole person figure

Impact of bisphenol A (BPA) on early embryo development in the marine mussel Mytilus galloprovincialis: Effects on gene transcription.

Bisphenol A (BPA), a monomer used in plastic manufacturing, is weakly estrogenic and a potential endocrine disruptor in mammals. Although it degrades quickly, it is pseudo-persistent in the environment because of continual inputs, with reported concentrations in aquatic environments between 0.0005 and 12 \u3bcg/L. BPA represents a potential concern for aquatic ecosystems, as shown by its reproductive and developmental effects in aquatic vertebrates. In invertebrates, endocrine-related effects of BPA were observed in different species and experimental conditions, with often conflicting results, indicating that the sensitivity to this compound can vary considerably among related taxa. In the marine mussel Mytilus galloprovincialis BPA was recently shown to affect early development at environmental concentrations. In this work, the possible effects of BPA on mussel embryos were investigated at the molecular level by evaluating transcription of 13 genes, selected on the basis of their biological functions in adult mussels. Gene expression was first evaluated in trocophorae and D-veligers (24 and 48 h post fertilization) grown in physiological conditions, in comparison with unfertilized eggs. Basal expressions showed a general up-regulation during development, with distinct transcript levels in trocophorae and D-veligers. Exposure of fertilized eggs to BPA (10 \u3bcg/L) induced a general upregulation at 24 h pf, followed by down regulation at 48 h pf. Mytilus Estrogen Receptors, serotonin receptor and genes involved in biomineralization (Carbonic Anydrase and Extrapallial Protein) were the most affected by BPA exposure. At 48 h pf, changes in gene expression were associated with irregularities in shell formation, as shown by scanning electron microscopy (SEM), indicating that the formation of the first shelled embryo, a key step in mussel development, represents a sensitive target for BPA. Similar results were obtained with the natural estrogen 17\u3b2-estradiol. The results demonstrate that BPA and E2 can affect Mytilus early development through dysregulation of gene transcription

Compressed Volumetric Heatmaps for Multi-Person 3D Pose Estimation

In this paper we present a novel approach for bottom-up multi-person 3D human pose estimation from monocular RGB images. We propose to use high resolution volumetric heatmaps to model joint locations, devising a simple and effective compression method to drastically reduce the size of this representation. At the core of the proposed method lies our Volumetric Heatmap Autoencoder, a fully-convolutional network tasked with the compression of ground-truth heatmaps into a dense intermediate representation. A second model, the Code Predictor, is then trained to predict these codes, which can be decompressed at test time to re-obtain the original representation. Our experimental evaluation shows that our method performs favorably when compared to state of the art on both multi-person and single-person 3D human pose estimation datasets and, thanks to our novel compression strategy, can process full-HD images at the constant runtime of 8 fps regardless of the number of subjects in the scene

Face-from-Depth for Head Pose Estimation on Depth Images

Depth cameras allow to set up reliable solutions for people monitoring and behavior understanding, especially when unstable or poor illumination conditions make unusable common RGB sensors. Therefore, we propose a complete framework for the estimation of the head and shoulder pose based on depth images only. A head detection and localization module is also included, in order to develop a complete end-to-end system. The core element of the framework is a Convolutional Neural Network, called POSEidon+, that receives as input three types of images and provides the 3D angles of the pose as output. Moreover, a Face-from-Depth component based on a Deterministic Conditional GAN model is able to hallucinate a face from the corresponding depth image. We empirically demonstrate that this positively impacts the system performances. We test the proposed framework on two public datasets, namely Biwi Kinect Head Pose and ICT-3DHP, and on Pandora, a new challenging dataset mainly inspired by the automotive setup. Experimental results show that our method overcomes several recent state-of-art works based on both intensity and depth input data, running in real-time at more than 30 frames per second

TrackFlow: Multi-Object Tracking with Normalizing Flows

The field of multi-object tracking has recently seen a renewed interest in the good old schema of tracking-by-detection, as its simplicity and strong priors spare it from the complex design and painful babysitting of tracking-by-attention approaches. In view of this, we aim at extending tracking-by-detection to multi-modal settings, where a comprehensive cost has to be computed from heterogeneous information e.g., 2D motion cues, visual appearance, and pose estimates. More precisely, we follow a case study where a rough estimate of 3D information is also available and must be merged with other traditional metrics (e.g., the IoU). To achieve that, recent approaches resort to either simple rules or complex heuristics to balance the contribution of each cost. However, i) they require careful tuning of tailored hyperparameters on a hold-out set, and ii) they imply these costs to be independent, which does not hold in reality. We address these issues by building upon an elegant probabilistic formulation, which considers the cost of a candidate association as the negative log-likelihood yielded by a deep density estimator, trained to model the conditional joint probability distribution of correct associations. Our experiments, conducted on both simulated and real benchmarks, show that our approach consistently enhances the performance of several tracking-by-detection algorithms.Comment: Accepted at ICCV 202

TrackFlow: Multi-Object Tracking with Normalizing Flows

The field of multi-object tracking has recently seen a renewed interest in the good old schema of tracking-by-detection, as its simplicity and strong priors spare it from the complex design and painful babysitting of tracking-by-attention approaches. In view of this, we aim at extending tracking-by-detection to multi-modal settings, where a comprehensive cost has to be computed from heterogeneous information e.g., 2D motion cues, visual appearance, and pose estimates. More precisely, we follow a case study where a rough estimate of 3D information is also available and must be merged with other traditional metrics (e.g., the IoU). To achieve that, recent approaches resort to either simple rules or complex heuristics to balance the contribution of each cost. However, i) they require careful tuning of tailored hyperparameters on a hold-out set, and ii) they imply these costs to be independent, which does not hold in reality. We address these issues by building upon an elegant probabilistic formulation, which considers the cost of a candidate association as the negative log-likelihood yielded by a deep density estimator, trained to model the conditional joint probability distribution of correct associations. Our experiments, conducted on both simulated and real benchmarks, show that our approach consistently enhances the performance of several tracking-by-detection algorithms