A system for learning statistical motion patterns

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction

A system for learning statistical motion patterns

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction

Learning by imitation with the STIFF-FLOP surgical robot: a biomimetic approach inspired by octopus movements

Transferring skills from a biological organism to a hyper-redundant system is a challenging task, especially when the two agents have very different structure/embodiment and evolve in different environments. In this article, we propose to address this problem by designing motion primitives in the form of probabilistic dynamical systems. We take inspiration from invertebrate systems in nature to seek for versatile representations of motion/behavior primitives in continuum robots. We take the perspective that the incredibly varied skills achieved by the octopus can guide roboticists toward the design of robust motor skill encoding schemes and present our ongoing work that aims at combining statistical machine learning, dynamical systems, and stochastic optimization to study the problem of transferring movement patterns from an octopus arm to a flexible surgical robot (STIFF-FLOP) composed of two modules with constant curvatures. The approach is tested in simulation by imitation and self-refinement of an octopus reaching motion

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

Self-supervised Spatio-temporal Representation Learning for Videos by Predicting Motion and Appearance Statistics

We address the problem of video representation learning without human-annotated labels. While previous efforts address the problem by designing novel self-supervised tasks using video data, the learned features are merely on a frame-by-frame basis, which are not applicable to many video analytic tasks where spatio-temporal features are prevailing. In this paper we propose a novel self-supervised approach to learn spatio-temporal features for video representation. Inspired by the success of two-stream approaches in video classification, we propose to learn visual features by regressing both motion and appearance statistics along spatial and temporal dimensions, given only the input video data. Specifically, we extract statistical concepts (fast-motion region and the corresponding dominant direction, spatio-temporal color diversity, dominant color, etc.) from simple patterns in both spatial and temporal domains. Unlike prior puzzles that are even hard for humans to solve, the proposed approach is consistent with human inherent visual habits and therefore easy to answer. We conduct extensive experiments with C3D to validate the effectiveness of our proposed approach. The experiments show that our approach can significantly improve the performance of C3D when applied to video classification tasks. Code is available at https://github.com/laura-wang/video_repres_mas.Comment: CVPR 201