Tracking a walking person using activity-guided annealed particle filtering

Tracking human pose using observations from less than three cameras is a challenging task due to ambiguity in the available image evidence. This work presents a method for tracking using a pre-trained model of activity to guidesampling within an Annealed Particle Filtering framework. The approach is an example of model-based analysis-by-synthesis and is capable of robust tracking from less than 3 cameras with reduced numbers of samples. We test the scheme on a common dataset containing ground truth mo-tion capture data and compare against quantitative results for standard Annealed Particle Filtering. We find lower ab-solute and relative error scores for both monocular and 2-camera sequences using 80% fewer particles. © 2008 IEEE

Behaviour based particle filtering for human articulated motion tracking

This paper presents an approach to human motion tracking using multiple pre-trained activity models for propagation of particles in Annealed Particle Filtering. Hidden Markov models are trained on dimensionally reduced joint angle data to produce models of activity. Particles are divided between models for propagation by HMM synthesis, before converging on a solution during the annealing process. The approach facilitates multi-view tracking of unknown subjects performing multiple known activities with low particle numbers

3D Human Motion Tracking and Pose Estimation using Probabilistic Activity Models

This thesis presents work on generative approaches to human motion tracking and pose estimation where a geometric model of the human body is used for comparison with observations. The existing generative tracking literature can be quite clearly divided between two groups. First, approaches that attempt to solve a difficult high-dimensional inference problem in the body model’s full or ambient pose space, recovering freeform or unknown activity. Second, approaches that restrict inference to a low-dimensional latent embedding of the full pose space, recovering activity for which training data is available or known activity. Significant advances have been made in each of these subgroups. Given sufficiently rich multiocular observations and plentiful computational resources, highdimensional approaches have been proven to track fast and complex unknown activities robustly. Conversely, low-dimensional approaches have been able to support monocular tracking and to significantly reduce computational costs for the recovery of known activity. However, their competing advantages have – although complementary – remained disjoint. The central aim of this thesis is to combine low- and high-dimensional generative tracking techniques to benefit from the best of both approaches. First, a simple generative tracking approach is proposed for tracking known activities in a latent pose space using only monocular or binocular observations. A hidden Markov model (HMM) is used to provide dynamics and constrain a particle-based search for poses. The ability of the HMM to classify as well as synthesise poses means that the approach naturally extends to the modelling of a number of different known activities in a single joint-activity latent space. Second, an additional low-dimensional approach is introduced to permit transitions between segmented known activity training data by allowing particles to move between activity manifolds. Both low-dimensional approaches are then fairly and efficiently combined with a simultaneous high-dimensional generative tracking task in the ambient pose space. This combination allows for the recovery of sequences containing multiple known and unknown human activities at an appropriate (dynamic) computational cost. Finally, a rich hierarchical embedding of the ambient pose space is investigated. This representation allows inference to progress from a single full-body or global non-linear latent pose space, through a number of gradually smaller part-based latent models, to the full ambient pose space. By preserving long-range correlations present in training data, the positions of occluded limbs can be inferred during tracking. Alternatively, by breaking the implied coordination between part-based models novel activity combinations, or composite activity, may be recovered

Pose Estimation For A Partially Observable Human Body From RGB-D Cameras

International audienceHuman pose estimation in realistic world conditions raises multiple challenges such as foreground extraction, background update and occlusion by scene objects. Most of existing approaches were demonstrated in controlled environments. In this paper, we propose a framework to improve the performance of existing tracking methods to cope with these problems. To this end, a robust and scalable framework is provided composed of three main stages. In the first one, a probabilistic occupancy grid updated with a Hidden Markov Model used to maintain an up-to-date background and to extract moving persons. The second stage uses component labelling to identify and track persons in the scene. The last stage uses an hierarchical particle filter to estimate the body pose for each moving person. Occlusions are handled by querying the occupancy grid to identify hidden body parts so that they can be discarded from the pose estimation process. We provide a parallel implementation that runs on CPU andGPU at 4 frames per second. We also validate the approach on our own dataset that consists of synchronized motion capture with a single RGB-D camera data of a person performing actions in challenging situations with severe occlusions generated by scene objects. We make this dataset available online

Metaheuristic Optimization Techniques for Articulated Human Tracking

Four adaptive metaheuristic optimization algorithms are proposed and demonstrated: Adaptive Parameter Particle Swarm Optimization (AP-PSO), Modified Artificial Bat (MAB), Differential Mutated Artificial Immune System (DM-AIS) and hybrid Particle Swarm Accelerated Artificial Immune System (PSO-AIS). The algorithms adapt their search parameters on the basis of the fitness of obtained solutions such that a good fitness value favors local search, while a poor fitness value favors global search. This efficient feedback of the solution quality, imparts excellent global and local search characteristic to the proposed algorithms. The algorithms are tested on the challenging Articulated Human Tracking (AHT) problem whose objective is to infer human pose, expressed in terms of joint angles, from a continuous video stream. The Particle Filter (PF) algorithms, widely applied in generative model based AHT, suffer from the 'curse of dimensionality' and 'degeneracy' challenges. The four proposed algorithms show stable performance throughout the course of numerical experiments. DM-AIS performs best among the proposed algorithms followed in order by PSO-AIS, AP-PSO, and MBA in terms of Most Appropriate Pose (MAP) tracking error. The MAP tracking error of the proposed algorithms is compared with four heuristic approaches: generic PF, Annealed Particle Filter (APF), Partitioned Sampled Annealed Particle Filter (PSAPF) and Hierarchical Particle Swarm Optimization (HPSO). They are found to outperform generic PF with a confidence level of 95%, PSAPF and HPSO with a confidence level of 85%. While DM-AIS and PSO-AIS outperform APF with a confidence level of 80%. Further, it is noted that the proposed algorithms outperform PSAPF and HPSO using a significantly lower number of function evaluations, 2500 versus 7200. The proposed algorithms demonstrate reduced particle requirements, hence improving computational efficiency and helping to alleviate the 'curse of dimensionality'. The adaptive nature of the algorithms is found to guide the whole swarm towards the optimal solution by sharing information and exploring a wider solution space which resolves the 'degeneracy' challenge. Furthermore, the decentralized structure of the algorithms renders them insensitive to accumulation of error and allows them to recover from catastrophic failures due to loss of image data, sudden change in motion pattern or discrete instances of algorithmic failure. The performance enhancements demonstrated by the proposed algorithms, attributed to their balanced local and global search capabilities, makes real-time AHT applications feasible. Finally, the utility of the proposed algorithms in low-dimensional system identification problems as well as high-dimensional AHT problems demonstrates their applicability in various problem domains

Advances in Monocular Exemplar-based Human Body Pose Analysis: Modeling, Detection and Tracking

Esta tesis contribuye en el análisis de la postura del cuerpo humano a partir de secuencias de imágenes adquiridas con una sola cámara. Esta temática presenta un amplio rango de potenciales aplicaciones en video-vigilancia, video-juegos o aplicaciones biomédicas. Las técnicas basadas en patrones han tenido éxito, sin embargo, su precisión depende de la similitud del punto de vista de la cámara y de las propiedades de la escena entre las imágenes de entrenamiento y las de prueba. Teniendo en cuenta un conjunto de datos de entrenamiento capturado mediante un número reducido de cámaras fijas, paralelas al suelo, se han identificado y analizado tres escenarios posibles con creciente nivel de dificultad: 1) una cámara estática paralela al suelo, 2) una cámara de vigilancia fija con un ángulo de visión considerablemente diferente, y 3) una secuencia de video capturada con una cámara en movimiento o simplemente una sola imagen estática

Single and multiple target tracking via hybrid mean shift/particle filter algorithms

This thesis is concerned with single and multiple target visual tracking algorithms and their application in the real world. While they are both powerful and general, one of the main challenges of tracking using particle filter-based algorithms is to manage the particle spread. Too wide a spread leads to dispersal of particles onto clutter, but limited spread may lead to difficulty when fast-moving objects and/or high-speed camera motion throw trackers away from their target(s). This thesis addresses the particle spread management problem. Three novel tracking algorithms are presented, each of which combines particle filtering and Kernel Mean Shift methods to produce more robust and accurate tracking. The first single target tracking algorithm, the Structured Octal Kernel Filter (SOK), combines Mean Shift (Comaniciu et al 2003) and Condensation (Isard and Blake 1998a). The spread of the particle set is handled by structurally placing the particles around the object, using eight particles arranged to cover the maximum area. Mean Shift is then applied to each particle to seek the global maxima. In effect, SOK uses intelligent switching between Mean Shift and particle filtering based on a confidence level. Though effective, it requires a threshold to be set and performs a somewhat inflexible search. The second single target tracking algorithm, the Kernel Annealed Mean Shift tracker (KAMS), uses an annealed particle filter (Deutscher et al 2000), but introduces a Mean Shift step to control particle spread. As a result, higher accuracy and robustness are achieved using fewer particles and annealing levels. Finally, KAMS is extended to create a multi-object tracking algorithm (MKAMS) by introducing an interaction filter to handle object collisions and occlusions. All three algorithms are compared experimentally with existing single/multiple object tracking algorithms. The evaluation procedure compares competing algorithms' robustness, accuracy and computational cost using both numerical measures and a novel application of McNemar's statistic. Results are presented on a wide variety of artificial and real image sequences

Spatiotemporal visual analysis of human actions

In this dissertation we propose four methods for the recognition of human activities. In all four of them, the representation of the activities is based on spatiotemporal features that are automatically detected at areas where there is a significant amount of independent motion, that is, motion that is due to ongoing activities in the scene. We propose the use of spatiotemporal salient points as features throughout this dissertation. The algorithms presented, however, can be used with any kind of features, as long as the latter are well localized and have a well-defined area of support in space and time. We introduce the utilized spatiotemporal salient points in the first method presented in this dissertation. By extending previous work on spatial saliency, we measure the variations in the information content of pixel neighborhoods both in space and time, and detect the points at the locations and scales for which this information content is locally maximized. In this way, an activity is represented as a collection of spatiotemporal salient points. We propose an iterative linear space-time warping technique in order to align the representations in space and time and propose to use Relevance Vector Machines (RVM) in order to classify each example into an action category. In the second method proposed in this dissertation we propose to enhance the acquired representations of the first method. More specifically, we propose to track each detected point in time, and create representations based on sets of trajectories, where each trajectory expresses how the information engulfed by each salient point evolves over time. In order to deal with imperfect localization of the detected points, we augment the observation model of the tracker with background information, acquired using a fully automatic background estimation algorithm. In this way, the tracker favors solutions that contain a large number of foreground pixels. In addition, we perform experiments where the tracked templates are localized on specific parts of the body, like the hands and the head, and we further augment the tracker’s observation model using a human skin color model. Finally, we use a variant of the Longest Common Subsequence algorithm (LCSS) in order to acquire a similarity measure between the resulting trajectory representations, and RVMs for classification. In the third method that we propose, we assume that neighboring salient points follow a similar motion. This is in contrast to the previous method, where each salient point was tracked independently of its neighbors. More specifically, we propose to extract a novel set of visual descriptors that are based on geometrical properties of three-dimensional piece-wise polynomials. The latter are fitted on the spatiotemporal locations of salient points that fall within local spatiotemporal neighborhoods, and are assumed to follow a similar motion. The extracted descriptors are invariant in translation and scaling in space-time. Coupling the neighborhood dimensions to the scale at which the corresponding spatiotemporal salient points are detected ensures the latter. The descriptors that are extracted across the whole dataset are subsequently clustered in order to create a codebook, which is used in order to represent the overall motion of the subjects within small temporal windows.Finally,we use boosting in order to select the most discriminative of these windows for each class, and RVMs for classification. The fourth and last method addresses the joint problem of localization and recognition of human activities depicted in unsegmented image sequences. Its main contribution is the use of an implicit representation of the spatiotemporal shape of the activity, which relies on the spatiotemporal localization of characteristic ensembles of spatiotemporal features. The latter are localized around automatically detected salient points. Evidence for the spatiotemporal localization of the activity is accumulated in a probabilistic spatiotemporal voting scheme. During training, we use boosting in order to create codebooks of characteristic feature ensembles for each class. Subsequently, we construct class-specific spatiotemporal models, which encode where in space and time each codeword ensemble appears in the training set. During testing, each activated codeword ensemble casts probabilistic votes concerning the spatiotemporal localization of the activity, according to the information stored during training. We use a Mean Shift Mode estimation algorithm in order to extract the most probable hypotheses from each resulting voting space. Each hypothesis corresponds to a spatiotemporal volume which potentially engulfs the activity, and is verified by performing action category classification with an RVM classifier

Stochastic optimization and interactive machine learning for human motion analysis

The analysis of human motion from visual data is a central issue in the computer vision research community as it enables a wide range of applications and it still remains a challenging problem when dealing with unconstrained scenarios and general conditions. Human motion analysis is used in the entertainment industry for movies or videogame production, in medical applications for rehabilitation or biomechanical studies. It is also used for human computer interaction in any kind of environment, and moreover, it is used for big data analysis from social networks such as Youtube or Flickr, to mention some of its use cases. In this thesis we have studied human motion analysis techniques with a focus on its application for smart room environments. That is, we have studied methods that will support the analysis of people behavior in the room, allowing interaction with computers in a natural manner and in general, methods that introduce computers in human activity environments to enable new kind of services but in an unobstrusive mode. The thesis is structured in two parts, where we study the problem of 3D pose estimation from multiple views and the recognition of gestures using range sensors. First, we propose a generic framework for hierarchically layered particle filtering (HPF) specially suited for motion capture tasks. Human motion capture problem generally involve tracking or optimization of high-dimensional state vectors where also one have to deal with multi-modal pdfs. HPF allow to overcome the problem by means of multiple passes through substate space variables. Then, based on the HPF framework, we propose a method to estimate the anthropometry of the subject, which at the end allows to obtain a human body model adjusted to the subject. Moreover, we introduce a new weighting function strategy for approximate partitioning of observations and a method that employs body part detections to improve particle propagation and weight evaluation, both integrated within the HPF framework. The second part of this thesis is centered in the detection of gestures, and we have focused the problem of reducing annotation and training efforts required to train a specific gesture. In order to reduce the efforts required to train a gesture detector, we propose a solution based on online random forests that allows training in real-time, while receiving new data in sequence. The main aspect that makes the solution effective is the method we propose to collect the hard negatives examples while training the forests. The method uses the detector trained up to the current frame to test on that frame, and then collects samples based on the response of the detector such that they will be more relevant for training. In this manner, training is more effective in terms of the number of annotated frames required.L'anàlisi del moviment humà a partir de dades visuals és un tema central en la recerca en visió per computador, per una banda perquè habilita un ampli espectre d'aplicacions i per altra perquè encara és un problema no resolt quan és aplicat en escenaris no controlats. L'analisi del moviment humà s'utilitza a l'indústria de l'entreteniment per la producció de pel·lícules i videojocs, en aplicacions mèdiques per rehabilitació o per estudis bio-mecànics. També s'utilitza en el camp de la interacció amb computadors o també per l'analisi de grans volums de dades de xarxes socials com Youtube o Flickr, per mencionar alguns exemples. En aquesta tesi s'han estudiat tècniques per l'anàlisi de moviment humà enfocant la seva aplicació en entorns de sales intel·ligents. És a dir, s'ha enfocat a mètodes que puguin permetre l'anàlisi del comportament de les persones a la sala, que permetin la interacció amb els dispositius d'una manera natural i, en general, mètodes que incorporin les computadores en espais on hi ha activitat de persones, per habilitar nous serveis de manera que no interfereixin en la activitat. A la primera part, es proposa un marc genèric per l'ús de filtres de partícules jeràrquics (HPF) especialment adequat per tasques de captura de moviment humà. La captura de moviment humà generalment implica seguiment i optimització de vectors d'estat de molt alta dimensió on a la vegada també s'han de tractar pdf's multi-modals. Els HPF permeten tractar aquest problema mitjançant multiples passades en subdivisions del vector d'estat. Basant-nos en el marc dels HPF, es proposa un mètode per estimar l'antropometria del subjecte, que a la vegada permet obtenir un model acurat del subjecte. També proposem dos nous mètodes per la captura de moviment humà. Per una banda, el APO es basa en una nova estratègia per les funcions de cost basada en la partició de les observacions. Per altra, el DD-HPF utilitza deteccions de parts del cos per millorar la propagació de partícules i l'avaluació de pesos. Ambdós mètodes són integrats dins el marc dels HPF. La segona part de la tesi es centra en la detecció de gestos, i s'ha enfocat en el problema de reduir els esforços d'anotació i entrenament requerits per entrenar un detector per un gest concret. Per tal de reduir els esforços requerits per entrenar un detector de gestos, proposem una solució basada en online random forests que permet l'entrenament en temps real, mentre es reben noves dades sequencialment. El principal aspecte que fa la solució efectiva és el mètode que proposem per obtenir mostres negatives rellevants, mentre s'entrenen els arbres de decisió. El mètode utilitza el detector entrenat fins al moment per recollir mostres basades en la resposta del detector, de manera que siguin més rellevants per l'entrenament. D'aquesta manera l'entrenament és més efectiu pel que fa al nombre de mostres anotades que es requereixen

Model figging of articulated objects

本稿では人体や手などに代表される多関節物体の三次元姿勢を画像から推定するモデルフィッティングの技術についてサーベイする。画像によるモデルフィッティングの枠組みを,1)推定に利用される画像特徴,2)画像と照合するモデルの表現と照合のパラメータ空間,3)照合時の評価関数と最適解の探索手法,にわけて多関節物体の三次元姿勢推定に特徴的な要素を上記三つの観点から比較整理することを試みる。In this paper, we present a survey report for the model fitting method to estimate3-D posture of articulated objects such as human body and hand. We decompose the model fitting framework into the following threee lements: 1)image feature, 2)model description and parameter space for model-image matching and 3)matching function and its optimization. From the viewpoint of these three issues, we try to compare the various methods of model fitting to each other and summarize them