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Classification of actions by human actors from video enables new technologies in diverse areas such as surveillance and content-based retrieval. We propose and evaluate alternative models, one based on feature-level fusion and the second on decision-level fusion. Both models employ direct classification - inferring from low-level features the nature of the(More)
Human action recognition has been an important topic in computer vision due to its many applications such as video surveillance, human machine interaction and video retrieval. One core problem behind these applications is automatically recognizing low-level actions and high-level activities of interest. The former is usually the basis for the latter. This(More)
Action recognition is an important component in human-machine interactive systems and video analysis. Besides low-level actions, temporal relationships are also important for many actions, which are not fully studied for recognizing actions. We model the temporal structure of low-level actions based on dense trajectory groups. Trajectory groups are a higher(More)
A Markov logic network (MLN) is a compact combination of logic representation of knowledge and probabilistic reasoning in Markov networks. We have seen its applications in different domains, however, few tried to explain or demonstrate the underneath reasons why MLN works. This paper gives an introduction to MLN using examples in the hope to help understand(More)
Wedescribe a real-time highway surveillance system (RHSS), which operates autonomously to collect statistics (speed and volume) and generates incident alerts (e.g., stopped vehicles). The system is designed to optimize long-term real-time performance accuracy. It also provides convenient integration to an existing surveillance infrastructure with different(More)
This paper describes a structure tensor based method to characterize activity in videos. Based on the relationship observed between structure tensors and covariance matrices, the distance between structure tensor matrices is computed by either a generalized eigenvalue or a Riemannian manifold. A histogram of distances between structure tensor matrices is(More)
A highly general and centralized reasoning framework which combines first-order-logic with Markov networks proposed to recognize both simple and complex activities. The generality and systematicity of the reasoning framework is characterized by a newly defined set of spatio-temporal and spatial semantic free low level event predicates(LLEs). With the new(More)
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