10 research outputs found
Robust human detection with occlusion handling by fusion of thermal and depth images from mobile robot
In this paper, a robust surveillance system to enable robots to detect humans in indoor environments is proposed. The proposed method is based on fusing information from thermal and depth images which allows the detection of human even under occlusion. The proposed method consists of three stages, pre-processing, ROI generation and object classification. A new dataset was developed to evaluate the performance of the proposed method. The experimental results show that the proposed method is able to detect multiple humans under occlusions and illumination variations
'Elbows Out' - Predictive tracking of partially occluded pose for Robot-Assisted dressing
© 2016 IEEE. Robots that can assist in the activities of daily living, such as dressing, may support older adults, addressing the needs of an aging population in the face of a growing shortage of care professionals. Using depth cameras during robot-assisted dressing can lead to occlusions and loss of user tracking, which may result in unsafe trajectory planning or prevent the planning task proceeding altogether. For the dressing task of putting on a jacket, which is addressed in this letter, tracking of the arm is lost when the user's hand enters the jacket, which may lead to unsafe situations for the user and a poor interaction experience. Using motion tracking data, free from occlusions, gathered from a human-human interaction study on an assisted dressing task, recurrent neural network models were built to predict the elbow position of a single arm based on other features of the user pose. The best features for predicting the elbow position were explored by using regression trees indicating the hips and shoulder as possible predictors. Engineered features were also created based on observations of real dressing scenarios and their effectiveness explored. Comparison between position and orientation-based datasets was also included in this study. A 12-fold cross-validation was performed for each feature set and repeated 20 times to improve statistical power. Using position-based data, the elbow position could be predicted with a 4.1 cm error but adding engineered features reduced the error to 2.4 cm. Adding orientation information to the data did not improve the accuracy and aggregating univariate response models failed to make significant improvements. The model was evaluated on Kinect data for a robot dressing task and although not without issues, demonstrates potential for this application. Although this has been demonstrated for jacket dressing, the technique could be applied to a number of different situations during occluded tracking
A Distributed Outdoor Video Surveillance System for Detection of Abnormal People Trajectories
Distributed surveillance systems are nowadays widely adopted to monitor large areas for security purposes. In this paper, we present a complete multicamera system designed for people tracking from multiple partially overlapped views and capable of inferring and detecting abnormal people trajectories. Detection and tracking are performed by means of background suppression and an appearance-based probabilistic approach. Objects' label ambiguities are geometrically solved and the concept of "normality" is learned from data using a robust statistical model based on Von Mises distributions. Abnormal trajectories are detected using a first-order Bayesian network and, for each abnormal event, the appearance of the subject from each view is logged. Experiments demonstrate that our system can process with real-time performance up to three cameras simultaneously in an unsupervised setup and under varying environmental conditions
Learning Social Etiquette: Human Trajectory Understanding In Crowded Scenes
Humans navigate crowded spaces such as a university campus by following common sense rules based on social etiquette. In this paper, we argue that in order to enable the design of new target tracking or trajectory forecasting methods that can take full advantage of these rules, we need to have access to better data in the first place. To that end, we contribute a new large-scale dataset that collects videos of various types of targets (not just pedestrians, but also bikers, skateboarders, cars, buses, golf carts) that navigate in a real world outdoor environment such as a university campus. Moreover, we introduce a new characterization that describes the âsocial sensitivityâ at which two targets interact. We use this characterization to define ânavigation stylesâ and improve both forecasting models and state-of-the-art multi-target trackingâwhereby the learnt forecasting models help the data association step
Multiple Object Tracking with Occlusion Handling
Object tracking is an important problem with wide ranging applications. The purpose is to detect object contours and track their motion in a video. Issues of concern are to be able to map objects correctly between two frames, and to be able to track through occlusion. This thesis discusses a novel framework for the purpose of object tracking which is inspired from image registration and segmentation models. Occlusion of objects is also detected and handled in this framework in an appropriate manner.
The main idea of our tracking framework is to reconstruct the sequence of images
in the video. The process involves deforming all the objects in a given image frame,
called the initial frame. Regularization terms are used to govern the deformation of
the shape of the objects. We use elastic and viscous fluid model as the regularizer. The reconstructed frame is formed by combining the deformed objects with respect to the depth ordering. The correct reconstruction is selected by parameters that minimize
the difference between the reconstruction and the consecutive frame, called the target frame. These parameters provide the required tracking information, such as the contour of the objects in the target frame including the occluded regions. The regularization term restricts the deformation of the object shape in the occluded region and thus gives an estimate of the object shape in this region. The other idea is to use a segmentation model as a measure in place of the frame difference measure.
This is separate from image segmentation procedure, since we use the segmentation
model in a tracking framework to capture object deformation. Numerical examples are
presented to demonstrate tracking in simple and complex scenes, alongwith occlusion
handling capability of our model. Segmentation measure is shown to be more robust with regard to accumulation of tracking error
Suivi visuel d'objets dans un réseau de caméras intelligentes embarquées
Multi-object tracking constitutes a major step in several computer vision applications. The requirements of these applications in terms of performance, processing time, energy consumption and the ease of deployment of a visual tracking system, make the use of low power embedded platforms essential. In this thesis, we designed a multi-object tracking system that achieves real time processing on a low cost and a low power embedded smart camera. The tracking pipeline was extended to work in a network of cameras with nonoverlapping field of views. The tracking pipeline is composed of a detection module based on a background subtraction method and on a tracker using the probabilistic Gaussian Mixture Probability Hypothesis Density (GMPHD) filter. The background subtraction, we developed, is a combination of the segmentation resulted from the Zipfian Sigma-Delta method with the gradient of the input image. This combination allows reliable detection with low computing complexity. The output of the background subtraction is processed using a connected components analysis algorithm to extract the features of moving objects. The features are used as input to an improved version of GMPHD filter. Indeed, the original GMPHD do not manage occlusion problems. We integrated two new modules in GMPHD filter to handle occlusions between objects. If there are no occlusions, the motion feature of objects is used for tracking. When an occlusion is detected, the appearance features of the objects are saved to be used for re-identification at the end of the occlusion. The proposed tracking pipeline was optimized and implemented on an embedded smart camera composed of the Raspberry Pi version 1 board and the camera module RaspiCam. The results show that besides the low complexity of the pipeline, the tracking quality of our method is close to the stat of the art methods. A frame rate of 15 â 30 was achieved on the smart camera depending on the image resolution. In the second part of the thesis, we designed a distributed approach for multi-object tracking in a network of non-overlapping cameras. The approach was developed based on the fact that each camera in the network runs a GMPHD filter as a tracker. Our approach is based on a probabilistic formulation that models the correspondences between objects as an appearance probability and space-time probability. The appearance of an object is represented by a vector of m dimension, which can be considered as a histogram. The space-time features are represented by the transition time between two input-output regions in the network and the transition probability from a region to another. Transition time is modeled as a Gaussian distribution with known mean and covariance. The distributed aspect of the proposed approach allows a tracking over the network with few communications between the cameras. Several simulations were performed to validate the approach. The obtained results are promising for the use of this approach in a real network of smart cameras.Le suivi dâobjets est de plus en plus utilisĂ© dans les applications de vision par ordinateur. Compte tenu des exigences des applications en termes de performance, du temps de traitement, de la consommation dâĂ©nergie et de la facilitĂ© du dĂ©ploiement des systĂšmes de suivi, lâutilisation des architectures embarquĂ©es de calcul devient primordiale. Dans cette thĂšse, nous avons conçu un systĂšme de suivi dâobjets pouvant fonctionner en temps rĂ©el sur une camĂ©ra intelligente de faible coĂ»t et de faible consommation Ă©quipĂ©e dâun processeur embarquĂ© ayant une architecture lĂ©gĂšre en ressources de calcul. Le systĂšme a Ă©tĂ© Ă©tendu pour le suivi dâobjets dans un rĂ©seau de camĂ©ras avec des champs de vision non-recouvrant. La chaĂźne algorithmique est composĂ©e dâun Ă©tage de dĂ©tection basĂ© sur la soustraction de fond et dâun Ă©tage de suivi utilisant un algorithme probabiliste Gaussian Mixture Probability Hypothesis Density (GMPHD). La mĂ©thode de soustraction de fond que nous avons proposĂ©e combine le rĂ©sultat fournie par la mĂ©thode Zipfian Sigma-Delta avec lâinformation du gradient de lâimage dâentrĂ©e dans le but dâassurer une bonne dĂ©tection avec une faible complexitĂ©. Le rĂ©sultat de soustraction est traitĂ© par un algorithme dâanalyse des composantes connectĂ©es afin dâextraire les caractĂ©ristiques des objets en mouvement. Les caractĂ©ristiques constituent les observations dâune version amĂ©liorĂ©e du filtre GMPHD. En effet, le filtre GMPHD original ne traite pas les occultations se produisant entre les objets. Nous avons donc intĂ©grĂ© deux modules dans le filtre GMPHD pour la gestion des occultations. Quand aucune occultation nâest dĂ©tectĂ©e, les caractĂ©ristiques de mouvement des objets sont utilisĂ©es pour le suivi. Dans le cas dâune occultation, les caractĂ©ristiques dâapparence des objets, reprĂ©sentĂ©es par des histogrammes en niveau de gris sont sauvegardĂ©es et utilisĂ©es pour la rĂ©-identification Ă la fin de lâoccultation. Par la suite, la chaĂźne de suivi dĂ©veloppĂ©e a Ă©tĂ© optimisĂ©e et implĂ©mentĂ©e sur une camĂ©ra intelligente embarquĂ©e composĂ©e de la carte Raspberry Pi version 1 et du module camĂ©ra RaspiCam. Les rĂ©sultats obtenus montrent une qualitĂ© de suivi proche des mĂ©thodes de lâĂ©tat de lâart et une cadence dâimages de 15 â 30 fps sur la camĂ©ra intelligente selon la rĂ©solution des images. Dans la deuxiĂšme partie de la thĂšse, nous avons conçu un systĂšme distribuĂ© de suivi multi-objet pour un rĂ©seau de camĂ©ras avec des champs non recouvrants. Le systĂšme prend en considĂ©ration que chaque camĂ©ra exĂ©cute un filtre GMPHD. Le systĂšme est basĂ© sur une approche probabiliste qui modĂ©lise la correspondance entre les objets par une probabilitĂ© dâapparence et une probabilitĂ© spatio-temporelle. Lâapparence dâun objet est reprĂ©sentĂ©e par un vecteur de m Ă©lĂ©ments qui peut ĂȘtre considĂ©rĂ© comme un histogramme. La caractĂ©ristique spatio-temporelle est reprĂ©sentĂ©e par le temps de transition des objets et la probabilitĂ© de transition dâun objet dâune rĂ©gion dâentrĂ©e-sortie Ă une autre. Le temps de transition est modĂ©lisĂ© par une loi normale dont la moyenne et la variance sont supposĂ©es ĂȘtre connues. Lâaspect distribuĂ© de lâapproche proposĂ©e assure un suivi avec peu de communication entre les noeuds du rĂ©seau. Lâapproche a Ă©tĂ© testĂ©e en simulation et sa complexitĂ© a Ă©tĂ© analysĂ©e. Les rĂ©sultats obtenus sont prometteurs pour le fonctionnement de lâapproche dans un rĂ©seau de camĂ©ras intelligentes rĂ©el
Probabilistic People Tracking for Occlusion Handling
This work presents a novel people tracking approach, able to cope with frequent shape changes and large occlusions. In particular, the tracks are described by means of probabilistic masks and appearance models. Occlusions due to other tracks or due to background objects and false occlusions are discriminated. The tracking system is general enough to be applied with any motion segmentation module, it can track people interacting each other and it maintains the pixel assignment to track even with large occlusions. At the same time, the update model is very reactive, so as to cope with sudden body motion and silhouette's shape changes. Due to its robustness, it has been used in many experiments of people behavior control in indoor situations