Bayesian Modeling of Dynamic Scenes for Object Detection

Abstract—Accurate detection of moving objects is an important precursor to stable tracking or recognition. In this paper, we present an object detection scheme that has three innovations over existing approaches. First, the model of the intensities of image pixels as independent random variables is challenged and it is asserted that useful correlation exists in intensities of spatially proximal pixels. This correlation is exploited to sustain high levels of detection accuracy in the presence of dynamic backgrounds. By using a nonparametric density estimation method over a joint domain-range representation of image pixels, multimodal spatial uncertainties and complex dependencies between the domain (location) and range (color) are directly modeled. We propose a model of the background as a single probability density. Second, temporal persistence is proposed as a detection criterion. Unlike previous approaches to object detection which detect objects by building adaptive models of the background, the foreground is modeled to augment the detection of objects (without explicit tracking) since objects detected in the preceding frame contain substantial evidence for detection in the current frame. Finally, the background and foreground models are used competitively in a MAP-MRF decision framework, stressing spatial context as a condition of detecting interesting objects and the posterior function is maximized efficiently by finding the minimum cut of a capacitated graph. Experimental validation of the proposed method is performed and presented on a diverse set of dynamic scenes. Index Terms—Object detection, kernel density estimation, joint domain range, MAP-MRF estimation. æ

Learning Manipulation under Physics Constraints with Visual Perception

Understanding physical phenomena is a key competence that enables humans and animals to act and interact under uncertain perception in previously unseen environments containing novel objects and their configurations. In this work, we consider the problem of autonomous block stacking and explore solutions to learning manipulation under physics constraints with visual perception inherent to the task. Inspired by the intuitive physics in humans, we first present an end-to-end learning-based approach to predict stability directly from appearance, contrasting a more traditional model-based approach with explicit 3D representations and physical simulation. We study the model's behavior together with an accompanied human subject test. It is then integrated into a real-world robotic system to guide the placement of a single wood block into the scene without collapsing existing tower structure. To further automate the process of consecutive blocks stacking, we present an alternative approach where the model learns the physics constraint through the interaction with the environment, bypassing the dedicated physics learning as in the former part of this work. In particular, we are interested in the type of tasks that require the agent to reach a given goal state that may be different for every new trial. Thereby we propose a deep reinforcement learning framework that learns policies for stacking tasks which are parametrized by a target structure.Comment: arXiv admin note: substantial text overlap with arXiv:1609.04861, arXiv:1711.00267, arXiv:1604.0006

Learning Manipulation under Physics Constraints with Visual Perception

Understanding physical phenomena is a key competence that enables humans and animals to act and interact under uncertain perception in previously unseen environments containing novel objects and their configurations. In this work, we consider the problem of autonomous block stacking and explore solutions to learning manipulation under physics constraints with visual perception inherent to the task. Inspired by the intuitive physics in humans, we first present an end-to-end learning-based approach to predict stability directly from appearance, contrasting a more traditional model-based approach with explicit 3D representations and physical simulation. We study the model's behavior together with an accompanied human subject test. It is then integrated into a real-world robotic system to guide the placement of a single wood block into the scene without collapsing existing tower structure. To further automate the process of consecutive blocks stacking, we present an alternative approach where the model learns the physics constraint through the interaction with the environment, bypassing the dedicated physics learning as in the former part of this work. In particular, we are interested in the type of tasks that require the agent to reach a given goal state that may be different for every new trial. Thereby we propose a deep reinforcement learning framework that learns policies for stacking tasks which are parametrized by a target structure

Investigating the latency cost of statistical learning of a Gaussian mixture simulating on a convolutional density network with adaptive batch size technique for background modeling

Background modeling is a promising field of study in video analysis, with a wide range of applications in video surveillance. Deep neural networks have proliferated in recent years as a result of effective learning-based approaches to motion analysis. However, these strategies only provide a partial description of the observed scenes' insufficient properties since they use a single-valued mapping to estimate the target background's temporal conditional averages. On the other hand, statistical learning in the imagery domain has become one of the most widely used approaches due to its high adaptability to dynamic context transformation, especially Gaussian Mixture Models. Specifically, these probabilistic models aim to adjust latent parameters to gain high expectation of realistically observed data; however, this approach only concentrates on contextual dynamics in short-term analysis. In a prolonged investigation, it is challenging so that statistical methods cannot reserve the generalization of long-term variation of image data. Balancing the trade-off between traditional machine learning models and deep neural networks requires an integrated approach to ensure accuracy in conception while maintaining a high speed of execution. In this research, we present a novel two-stage approach for detecting changes using two convolutional neural networks in this work. The first architecture is based on unsupervised Gaussian mixtures statistical learning, which is used to classify the salient features of scenes. The second one implements a light-weighted pipeline of foreground detection. Our two-stage system has a total of approximately 3.5K parameters but still converges quickly to complex motion patterns. Our experiments on publicly accessible datasets demonstrate that our proposed networks are not only capable of generalizing regions of moving objects with promising results in unseen scenarios, but also competitive in terms of performance quality and effectiveness foreground segmentation. Apart from modeling the data's underlying generator as a non-convex optimization problem, we briefly examine the communication cost associated with the network training by using a distributed scheme of data-parallelism to simulate a stochastic gradient descent algorithm with communication avoidance for parallel machine learnin

Robust Subspace Learning: Robust PCA, Robust Subspace Tracking, and Robust Subspace Recovery

PCA is one of the most widely used dimension reduction techniques. A related easier problem is "subspace learning" or "subspace estimation". Given relatively clean data, both are easily solved via singular value decomposition (SVD). The problem of subspace learning or PCA in the presence of outliers is called robust subspace learning or robust PCA (RPCA). For long data sequences, if one tries to use a single lower dimensional subspace to represent the data, the required subspace dimension may end up being quite large. For such data, a better model is to assume that it lies in a low-dimensional subspace that can change over time, albeit gradually. The problem of tracking such data (and the subspaces) while being robust to outliers is called robust subspace tracking (RST). This article provides a magazine-style overview of the entire field of robust subspace learning and tracking. In particular solutions for three problems are discussed in detail: RPCA via sparse+low-rank matrix decomposition (S+LR), RST via S+LR, and "robust subspace recovery (RSR)". RSR assumes that an entire data vector is either an outlier or an inlier. The S+LR formulation instead assumes that outliers occur on only a few data vector indices and hence are well modeled as sparse corruptions.Comment: To appear, IEEE Signal Processing Magazine, July 201

Selective Subtraction: An Extension of Background Subtraction

Background subtraction or scene modeling techniques model the background of the scene using the stationarity property and classify the scene into two classes of foreground and background. In doing so, most moving objects become foreground indiscriminately, except for perhaps some waving tree leaves, water ripples, or a water fountain, which are typically learned as part of the background using a large training set of video data. Traditional techniques exhibit a number of limitations including inability to model partial background or subtract partial foreground, inflexibility of the model being used, need for large training data and computational inefficiency. In this thesis, we present our work to address each of these limitations and propose algorithms in two major areas of research within background subtraction namely single-view and multi-view based techniques. We first propose the use of both spatial and temporal properties to model a dynamic scene and show how Mapping Convergence framework within Support Vector Mapping Convergence (SVMC) can be used to minimize training data. We also introduce a novel concept of background as the objects other than the foreground, which may include moving objects in the scene that cannot be learned from a training set because they occur only irregularly and sporadically, e.g. a walking person. We propose a selective subtraction method as an alternative to standard background subtraction, and show that a reference plane in a scene viewed by two cameras can be used as the decision boundary between foreground and background. In our definition, the foreground may actually occur behind a moving object. Our novel use of projective depth as a decision boundary allows us to extend the traditional definition of background subtraction and propose a much more powerful framework. Furthermore, we show that the reference plane can be selected in a very flexible manner, using for example the actual moving objects in the scene, if needed. We present diverse set of examples to show that: (i) the technique performs better than standard background subtraction techniques without the need for training, camera calibration, disparity map estimation, or special camera configurations; (ii) it is potentially more powerful than standard methods because of its flexibility of making it possible to select in real-time what to filter out as background, regardless of whether the object is moving or not, or whether it is a rare event or a frequent one; (iii) the technique can be used for a variety of situations including when images are captured using stationary cameras or hand-held cameras and for both indoor and outdoor scenes. We provide extensive results to show the effectiveness of the proposed framework in a variety of very challenging environments