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Human interaction dynamics are known to play an important role in the development of robust pedestrian trackers that are applicable to a variety of applications in video surveillance. Traditional approaches to pedestrian tracking assume that each pedestrian walks independently and the tracker predicts the location based on an underlying motion model, such as a constant velocity or autoregressive model. Recent approaches have begun to leverage interaction, especially by modeling the repulsion force, among pedestrians to improve motion predictions. However, human interaction is more complex and is influenced by both repulsion and attraction effects. This motivates the use of a more complex human interaction model for pedestrian tracking. In this paper, we propose a novel visual tracking method by leveraging complex social interactions. We present an algorithm that decomposes social interactions into multiple potential interaction modes. We integrate these multiple social interaction modes into an interactive Markov Chain Monte Carlo tracker. We demonstrate how the developed method translates into a more informed motion prediction, resulting in a robust tracking performance. We test our method on videos from unconstrained outdoor environments and compare it against popular multi-object trackers.

Face recognition is a challenging task, especially when low-resolution images or image sequences are used. A decrease in image resolution results in a loss of facial high frequency components leading to a decrease in recognition rates. In this paper, we propose a new method for super-resolution by building a dictionary of high-frequency components in the facial data, which are added to a low-resolution input image to create a super-resolved image. Our method is different from existing methods as we estimate the high-frequency components, rather than studying the direct relationship between the high- and low-resolution images. Quantitative and qualitative results are reported for both synthetic and surveillance facial image databases.

This paper presents a simple and computationally efficient framework for human action recognition based on modeling the motion of human body parts. Intuitively, a collective understanding of human part movements can lead to better understanding and representation of any human action. In this paper, we propose a generative representation of the motion of the human body parts to learn and classify human actions. The proposed representation combines the advantages of both local and global representations, encoding the relevant motion information as well as being robust to local appearance changes. Our work is motivated by the pictorial structures model and the framework of sparse representations for recognition. Human part movements are represented efficiently through quantization in the polar space. The key discrimination within each action is efficiently encoded by sparse representation to perform classification. The proposed method is evaluated on both the KTH and the UCF action datasets and the results are compared against other state-of-the-art methods.

In this paper, we present a non-intrusive method for human motion estimation from a monocular video camera for the teleoperation of ROBONAUT (ROBOtic astroNAUT). ROBONAUT is an anthropomorphic robot developed at NASA - JSC, which is capable of dextrous, human- like maneuvers to handlecommonextravehicularactivity tools. The humanoperatoris represented using an articulated three-dimensional model consisting of rigid links connected by spherical joints. The shape of a link is described by a triangular mesh and its motion by six parameters: one three-dimensional translation vector and three rotation angles. The motion parameters of the links are estimated by maximizing the conditional probability of the frame-to-frame intensity differences at observation points. The algorithm was applied to real test sequences of a moving arm with very encouraging results. Specifically, the mean error for the derived wrist position (using the estimated motion parameters) was 0.57 0.31 cm. The motion estimates were used to remotely command a robonaut simulation developed at NASA - JSC.

In this paper, we construct a new class of deformable models using new biorthogonal wavelets, named Generalized Hermite Distributed Approximating Functional (g-HDAF) Wavelets. The scaling functions of this new family are symmetric and the corresponding wavelets optimize their smoothness for a given number of vanishing moments. In addition, we embed these multiresolution deformable models to the physics-based deformable model framework and use them for fitting 3D range data. We have performed a number of experiments with both synthetic and real data with very encouraging results.