Despite remarkable progress in various human behavior perception tasks, head gesture recognition (HGR) has received limited attention in terms of datasets, benchmarks, and methods. In this work, we aim to address this gap and make two main contributions. First, we densely annotated the existing large-scale conversational dataset CCDb with diverse head gesture categories. This results in the CCDb-HG dataset, which can serve as a comprehensive benchmark for HGR research. Secondly, while previous gesture recognition methods have largely relied on head pose or facial landmarks as input, we propose to explore in addition the use of gaze to resolve ambiguous cases. This follows from the fact that head dynamics in interactions is driven by two main functions: communication (i.e. head gestures) and attention (i.e. gazing at other people or objects of interest). In fact, the head dynamics associated with attention activities can be confused for communication gestures, even though the gaze patterns are quite different in the two cases. In addition, we study several geometric and temporal data augmentation techniques to improve the generalization across novel viewpoints, as well as different model architectures to establish baseline performance on CCDb-HG. Our findings provide insights into various aspects of HGR and motivate further research in this field. To facilitate reproducibility, we will release the CCDb-HG annotations, code, and HGR models.

Pushing is an essential motor skill involved in several manipulation tasks, and has been an important research topic in robotics. Recent works have shown that Deep Q-Networks (DQNs) can learn pushing policies (when, where to push, and how) to solve manipulation tasks, potentially in synergy with other skills (e.g. grasping). Nevertheless, DQNs often assume a fixed setting and task, which may limit their deployment in practice. Furthermore, they suffer from sparse-gradient backpropagation when the action space is very large, a problem exacerbated by the fact that they are trained to predict state-action values based on a single reward function aggregating several facets of the task, rendering the model training challenging. To address these issues, we propose a multi-head target-parameterized DQN to learn robotic manipulation tasks, in particular pushing policies, and make the following contributions: i) we show that learning to predict different reward and task aspects can be beneficial compared to predicting a single value function where reward factors are not disentangled; ii) we study several alternatives to generalize a policy by encoding the target parameters either into the network layers or visually in the input; iii) we propose a kernelized version of the loss function, allowing to obtain better, faster and more stable training performance. Extensive experiments on simulations validate our design choices, and we show that our architecture learned on simulated data can achieve high performance in a real-robot setup involving a Franka Emika robot arm and unseen objects.

We propose to leverage recent advances in reliable 2D pose estimation with Convolutional Neural Networks (CNN) to estimate the 3D pose of people from depth images in multi-person Human-Robot Interaction (HRI) scenarios. Our method is based on the observation that using the depth information to obtain 3D lifted points from 2D body landmark detections provides a rough estimate of the true 3D human pose, thus requiring only a refinement step. In that line our contributions are threefold. (i) we propose to perform 3D pose estimation from depth images by decoupling 2D pose estimation and 3D pose refinement; (ii) we propose a deep-learning approach that regresses the residual pose between the lifted 3D pose and the true 3D pose; (iii) we show that despite its simplicity, our approach achieves very competitive results both in accuracy and speed on two public datasets and is therefore appealing for multi-person HRI compared to recent state-of-the-art methods.

Achieving robust multi-person 2D body landmark localization and pose estimation is essential for human behavior and interaction understanding as encountered for instance in HRI settings. Accurate methods have been proposed recently, but they usually rely on rather deep Convolutional Neural Network (CNN) architecture, thus requiring large computational and training resources. In this paper, we investigate different architectures and methodologies to address these issues and achieve fast and accurate multi-person 2D pose estimation. To foster speed, we propose to work with depth images, whose structure contains sufficient information about body landmarks while being simpler than textured color images and thus potentially requiring less complex CNNs for processing. In this context, we make the following contributions. i) we study several CNN architecture designs combining pose machines relying on the cascade of detectors concept with lightweight and efficient CNN structures; ii) to address the need for large training datasets with high variability, we rely on semi-synthetic data combining multi-person synthetic depth data with real sensor backgrounds; iii) we explore domain adaptation techniques to address the performance gap introduced by testing on real depth images; iv) to increase the accuracy of our fast lightweight CNN models, we investigate knowledge distillation at several architecture levels which effectively enhance performance. Experiments and results on synthetic and real data highlight the impact of our design choices, providing insights into methods addressing standard issues normally faced in practical applications, and resulting in architectures effectively matching our goal in both performance and speed.

We introduce a new dataset, dubbed UNICITY1, for the task of detecting people in security airlocks in top view depth images. If security companies have been relying on computer systems and algorithms for a long time, very few are trusting artificial intelligence and more specifically machine learning approaches in production environments. We are confident that the recent advances in these domains, especially with the democratization of deep learning, will open new horizons for security systems. We release this dataset to encourage the development of such approaches in the scientific community.UNICITY consists of 58k images collected from 65 recorded sequences with one or two people performing different behaviors including attacks and trickeries (e.g. tailgating2). It also provides full annotation of people such as the location of head and shoulders. As as result, UNICITY is perfectly suited for training and adapting machine learning algorithms for video surveillance applications. This paper presents the data collection, an evaluation protocol, as well as two baseline methods for attack detection.

We propose a deep-learning approach for people detection on depth imagery. The approach is designed to be deployed as an autonomous appliance for identifying people attacks and intrusion in video surveillance scenarios. To this end, we propose a fully-convolutional and sequential network, named WatchNet, that localizes people in depth images by predicting human body landmarks such as head and shoulders. We use a large synthetic dataset to train the network with abundant data and generate automatic annotations. Adaptation to real data is performed via fine tuning with real depth images.The proposed method is validated in a novel and challenging database with about 29k top view images collected from several sequences including different people assaults. A comparative evaluation is given between our approach and other standard methods, showing remarkable detection results and efficiency. The network runs in 10 and 28 FPS using CPU and GPU, respectively.

We propose to combine recent Convolutional Neural Networks (CNN) models with depth imaging to obtain a reliable and fast multi-person pose estimation algorithm applicable to Human Robot Interaction (HRI) scenarios. Our hypothesis is that depth images contain less structures and are easier to process than RGB images while keeping the required information for human detection and pose inference, thus allowing the use of simpler networks for the task. Our contributions are threefold. (i) we propose a fast and efficient network based on residual blocks (called RPM) for body landmark localization from depth images; (ii) we created a public dataset DIH comprising more than 170k synthetic images of human bodies with various shapes and viewpoints as well as real (annotated) data for evaluation; (iii) we show that our model trained on synthetic data from scratch can perform well on real data, obtaining similar results to larger models initialized with pre-trained networks. It thus provides a good trade-off between performance and computation. Experiments on real data demonstrate the validity of our approach.

In this paper we introduce the Boosted Random Ferns (BRFs) to rapidly build discriminative classifiers for learning and detecting object categories. At the core of our approach we use standard random ferns, but we introduce four main innovations that let us bring ferns from an instance to a category level, and still retain efficiency. First, we define binary features on the histogram of oriented gradients-domain (as opposed to intensity-), allowing for a better representation of intra-class variability. Second, both the positions where ferns are evaluated within the sliding window, and the location of the binary features for each fern are not chosen completely at random, but instead we use a boosting strategy to pick the most discriminative combination of them. This is further enhanced by our third contribution, that is to adapt the boosting strategy to enable sharing of binary features among different ferns, yielding high recognition rates at a low computational cost. And finally, we show that training can be performed online, for sequentially arriving images. Overall, the resulting classifier can be very efficiently trained, densely evaluated for all image locations in about 0.1 seconds, and provides detection rates similar to competing approaches that require expensive and significantly slower processing times. We demonstrate the effectiveness of our approach by thorough experimentation in publicly available datasets in which we compare against state-of-the-art, and for tasks of both 2D detection and 3D multi-view estimation.

In this paper, we propose a novel Aerial Social Force Model (ASFM) that allows autonomous flying robots to accompany humans in urban environments in a safe and comfortable manner. To date, we are not aware of other state-of-the-art method that accomplish this task. The proposed approach is a 3D version of the Social Force Model (SFM) for the field of aerial robots which includes an interactive human-robot navigation scheme capable of predicting human motions and intentions so as to safely accompany them to their final destination. ASFM also introduces a new metric to fine-tune the parameters of the force model, and to evaluate the performance of the aerial robot companion based on comfort and distance between the robot and humans. The presented approach is extensively validated in diverse simulations and real experiments, and compared against other similar works in the literature. ASFM attains remarkable results and shows that it is a valuable framework for social robotics applications, such as guiding people or human-robot interaction.

We propose a robust and efficient method to estimate the pose of a camera with respect to complex 3D textured models of the environment that can potentially contain more than 100; 000 points. To tackle this problem we follow a top down approach where we combine high-level deep network classifiers with low level geometric approaches to come up with a solution that is fast, robust and accurate. Given an input image, we initially use a pre-trained deep network to compute a rough estimation of the camera pose. This initial estimate constrains the number of 3D model points that can be seen from the camera viewpoint. We then establish 3D-to-2D correspondences between these potentially visible points of the model and the 2D detected image features. Accurate pose estimation is finally obtained from the 2D-to-3D correspondences using a novel PnP algorithm that rejects outliers without the need to use a RANSAC strategy, and which is between 10 and 100 times faster than other methods that use it. Two real experiments dealing with very large and complex 3D models demonstrate the effectiveness of the approach.

We propose an efficient Human Robot Interaction approach to efficiently model the appearance of all relevant objects in robot's environment. Given an input video stream recorded while the robot is navigating, the user just needs to annotate a very small number of frames to build specific classifiers for each of the objects of interest. At the core of the method, there are several random ferns classifiers that share the same features and are updated online. The resulting methodology is fast (runs at 8 fps), versatile (it can be applied to unconstrained scenarios), scalable (real experiments show we can model up to 30 different object classes), and minimizes the amount of human intervention by leveraging the uncertainty measures associated to each classifier. We thoroughly validate the approach on synthetic data and on real sequences acquired with a mobile platform in outdoor and challenging scenarios containing a multitude of different objects. We show that the human can, with minimal effort, provide the robot with a detailed model of the objects in the scene.

We present a method for efficiently detecting natural landmarks that can handle scenes with highly repetitive patterns and targets progressively changing its appearance. At the core of our approach lies a Random Ferns classifier, that models the posterior probabilities of different views of the target using multiple and independent Ferns, each containing features at particular positions of the target. A Shannon entropy measure is used to pick the most informative locations of these features. This minimizes the number of Ferns while maximizing its discriminative power, allowing thus, for robust detections at low computational costs. In addition, after offline initialization, the new incoming detections are used to update the posterior probabilities on the fly, and adapt to changing appearances that can occur due to the presence of shadows or occluding objects. All these virtues, make the proposed detector appropriate for UAV navigation. Besides the synthetic experiments that will demonstrate the theoretical benefits of our formulation, we will show applications for detecting landing areas in regions with highly repetitive patterns, and specific objects under the presence of cast shadows or sudden camera motions.

During the last decade, there has been a growing interest in making autonomous social robots able to interact with people. However, there are still many open issues regarding the social capabilities that robots should have in order to perform these interactions more naturally. In this paper we present the results of several experiments conducted at the Barcelona Robot Lab in the campus of the “Universitat Politècnica de Catalunya” in which we have analyzed different important aspects of the interaction between a mobile robot and nontrained human volunteers. First, we have proposed different robot behaviors to approach a person and create an engagement with him/her. In order to perform this task we have provided the robot with several perception and action capabilities, such as that of detecting people, planning an approach and verbally communicating its intention to initiate a conversation. Once the initial engagement has been created, we have developed further communication skills in order to let people assist the robot and improve its face recognition system. After this assisted and online learning stage, the robot becomes able to detect people under severe changing conditions, which, in turn enhances the number and the manner that subsequent human-robot interactions are performed.

We propose an efficient method for object localization and 3D pose estimation. A two-step approach is used. In the first step, a pose estimator is evaluated in the input images in order to estimate potential object locations and poses. These candidates are then validated, in the second step, by the corresponding pose-specific classifier. The result is a detection approach that avoids the inherent and expensive cost of testing the complete set of specific classifiers over the entire image. A further speedup is achieved by feature sharing. Features are computed only once and are then used for evaluating the pose estimator and all specific classifiers. The proposed method has been validated on two public datasets for the problem of detecting of cars under several views. The results show that the proposed approach yields high detection rates while keeping efficiency. © 2011. The copyright of this document resides with its authors.

We present a new approach for building an efficient and robust classifier for the two class problem, that localizes objects that may appear in the image under different orientations. In contrast to other works that address this problem using multiple classifiers, each one specialized for a specific orientation, we propose a simple two-step approach with an estimation stage and a classification stage. The estimator yields an initial set of potential object poses that are then validated by the classifier. This methodology allows reducing the time complexity of the algorithm while classification results remain high. The classifier we use in both stages is based on a boosted combination of Random Ferns over local histograms of oriented gradients (HOGs), which we compute during a preprocessing step. Both the use of supervised learning and working on the gradient space makes our approach robust while being efficient at run-time. We show these properties by thorough testing on standard databases and on a new database made of motorbikes under planar rotations, and with challenging conditions such as cluttered backgrounds, changing illumination conditions and partial occlusions.

Cast shadows add additional difficulties on detecting objects because they locally modify image intensity and color. Shadows may appear or disappear in an image when the object, the camera, or both are free to move through a scene. This work evaluates the performance of an object detection method based on boosted HOG paired with three different image representations in outdoor video sequences. We follow and extend on the taxonomy from van de Sande with considerations on the constraints assumed by each descriptor on the spatial variation of the illumination. We show that the intrinsic image representation consistently gives the best results. This proves the usefulness of this representation for object detection in varying illumination conditions, and supports the idea that in practice local assumptions in the descriptors can be violated.

We propose a new algorithm for detecting multiple object categories that exploits the fact that different categories may share common features but with different geometric distributions. This yields an efficient detector which, in contrast to existing approaches, considerably reduces the computation cost at runtime, where the feature computation step is traditionally the most expensive. More specifically, at the learning stage we compute common features by applying the same Random Ferns over the Histograms of Oriented Gradients on the training images. We then apply a boosting step to build discriminative weak classifiers, and learn the specific geometric distribution of the Random Ferns for each class. At runtime, only a few Random Ferns have to be densely computed over each input image, and their geometric distribution allows performing the detection. The proposed method has been validated in public datasets achieving competitive detection results, which are comparable with state-of-the-art methods that use specific features per class.

In this work we present a robust detection method in outdoor scenes under cast shadows using color based invariant gradients in combination with HoG local features. The method achieves good detection rates in urban scene classification and person detection outperforming traditional methods based on intensity gradient detectors which are sensible to illumination variations but not to cast shadows. The method uses color based invariant gradients that emphasize material changes and extract relevant and invariant features for detection while neglecting shadow contours. This method allows to train and detect objects and scenes independently of scene illumination, cast and self shadows. Moreover, it allows to do training in one shot, that is, when the robot visits the scene for the first time.