Context is an essential capability for robots that are to be as adaptive as possible in challenging environments. Although there are many context modeling efforts, they assume a fixed structure and number of contexts. In this paper, we propose an incremental deep model that extends Restricted Boltzmann Machines. Our model gets one scene at a time, and gradually extends the contextual model when necessary, either by adding a new context or a new context layer to form a hierarchy. We show on a scene classification benchmark that our method converges to a good estimate of the contexts of the scenes, and performs better or on-par on several tasks compared to other incremental models or non-incremental models.
In computer vision and pattern recognition, on the other hand, models integrated context into many problems such as object recognition @cite_20 @cite_13 , activity recognition @cite_15 using probabilistic graphical models, such as Markov Random Field, Conditional Random Field, or Bayesian Networks. In these models, contextual information was provided mostly through local interactions between predictions.
@cite_15 @cite_13 @cite_20
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Recognizing objects in images is an active area of research in computer vision. In the last two decades, there has been much progress and there are already object recognition systems operating in commercial products. However, most of the algorithms for detecting objects perform an exhaustive search across all locations and scales in the image comparing local image regions with an object model. That approach ignores the semantic structure of scenes and tries to solve the recognition problem by brute force. In the real world, objects tend to covary with other objects, providing a rich collection of contextual associations. These contextual associations can be used to reduce the search space by looking only in places in which the object is expected to be; this also increases performance, by rejecting patterns that look like the target but appear in unlikely places. Most modeling attempts so far have defined the context of an object in terms of other previously recognized objects. The drawback of this approach is that inferring the context becomes as difficult as detecting each object. An alternative view of context relies on using the entire scene information holistically. This approach is algorithmically attractive since it dispenses with the need for a prior step of individual object recognition. In this paper, we use a probabilistic framework for encoding the relationships between context and object properties and we show how an integrated system provides improved performance. We view this as a significant step toward general purpose machine vision systems. Daily living activity recognition can be exploited to benefit mobile and ubiquitous computing applications. Techniques so far are mature to recognize simple actions. Due to the characteristics of diversity and uncertainty in daily living applications, most existing complex activity recognition approaches have notable limitations. First, graphical model-based approaches still lack sufficient expressive power to model rich temporal relations among activities. Second, it would be rather difficult for graphical model-based approaches to build a unified model for achieving multiple types of tasks. Third, current semantic-based approaches often fail to capture uncertainties. Fourth, formulae in these semantic-based approaches are often manually encoded. Meanwhile, it is impractical to handcraft each formula accurately in daily living scenarios where temporal relations among activities are intricate. To address these issues, we present a probabilistic semantic-based framework that combines Markov logic network with 15 temporal and hierarchical relations to explicitly perform diverse inference tasks of daily living in a unified manner. Advanced pattern mining techniques are introduced to automatically learn the propositional logic rules of intricate relations as well as their weights. Experimental results show that by logical reasoning with the mined temporal dependencies under uncertainty, the proposed model leads to an improved performance, particularly when recognizing complex activities involving the incomplete or incorrect observations of atomic actions. HighlightsA probabilistic semantic-based framework is presented to explicitly perform diverse inference tasks.The framework combines Markov logic networks with 15 relations between activities.Advanced pattern mining techniques are introduced to learn intricate temporal rules.Performances can be improved by logical reasoning with the mined rules under uncertainty.Our approach is robust to the incomplete or incorrect observations of intervals. Several visual attention models have been proposed for describing eye movements over simple stimuli and tasks such as free viewing or visual search. Yet, to date, there exists no computational framework that can reliably mimic human gaze behavior in more complex environments and tasks such as urban driving. In addition, benchmark datasets, scoring techniques, and top-down model architectures are not yet well understood. In this paper, we describe new task-dependent approaches for modeling top-down overt visual attention based on graphical models for probabilistic inference and reasoning. We describe a dynamic Bayesian network that infers probability distributions over attended objects and spatial locations directly from observed data. Probabilistic inference in our model is performed over object-related functions that are fed from manual annotations of objects in video scenes or by state-of-the-art object detection recognition algorithms. Evaluating over approximately 3 h (approximately 315 000 eye fixations and 12 000 saccades) of observers playing three video games (time-scheduling, driving, and flight combat), we show that our approach is significantly more predictive of eye fixations compared to: 1) simpler classifier-based models also developed here that map a signature of a scene (multimodal information from gist, bottom-up saliency, physical actions, and events) to eye positions; 2) 14 state-of-the-art bottom-up saliency models; and 3) brute-force algorithms such as mean eye position. Our results show that the proposed model is more effective in employing and reasoning over spatio-temporal visual data compared with the state-of-the-art. In this paper, we propose a mathematical framework to jointly model related activities with both motion and context information for activity recognition and anomaly detection. This is motivated from observations that activities related in space and time rarely occur independently and can serve as context for each other. The spatial and temporal distribution of different activities provides useful cues for the understanding of these activities. We denote the activities occurring with high frequencies in the database as normal activities. Given training data which contains labeled normal activities, our model aims to automatically capture frequent motion and context patterns for each activity class, as well as each pair of classes, from sets of predefined patterns during the learning process. Then, the learned model is used to generate globally optimum labels for activities in the testing videos. We show how to learn the model parameters via an unconstrained convex optimization problem and how to predict the correct labels for a testing instance consisting of multiple activities. The learned model and generated labels are used to detect anomalies whose motion and context patterns deviate from the learned patterns. We show promising results on the VIRAT Ground Dataset that demonstrates the benefit of joint modeling and recognition of activities in a wide-area scene and the effectiveness of the proposed method in anomaly detection.