Extracting Moving People from Internet Videos
We propose a fully automatic framework to detect and extract arbitrary human motion volumes from real-world videos collected from YouTube. Our system is composed of two stages. A person detector is first applied to provide crude information about the possible locations of humans. Then a constrained clustering algorithm groups the detections and rejects false positives based on the appearance similarity and spatio-temporal coherence. In the second stage, we apply a top-down pictorial structure model to complete the extraction of the humans in arbitrary motion. During this procedure, a density propagation technique based on a mixture of Gaussians is employed to propagate temporal information in a principled way. This method reduces greatly the search space for the measurement in the inference stage. We demonstrate the initial success of this framework both quantitatively and qualitatively by using a number of YouTube videos.

title: What, where and who? Classifying events by scene and object recognition
abstract: We propose a first attempt to classify events in static images by integrating scene and object categorizations. We define an event in a static image as a human activity taking place in a specific environment. In this paper, we use a number of sport games such as snow boarding, rock climbing or badminton to demonstrate event classification. Our goal is to classify the event in the image as well as to provide a number of semantic labels to the objects and scene environment within the image. For example, given a rowing scene, our algorithm recognizes the event as rowing by classifying the environment as a lake and recognizing the critical objects in the image as athletes, rowing boat, water, etc. We achieve this integrative and holistic recognition through a generative graphical model. We have assembled a highly challenging database of 8 widely varied sport events. We show that our system is capable of classifying these event classes at 73.4% accuracy. While each component of the model contributes to the final recognition, using scene or objects alone cannot achieve this performance.

title: Audio-Visual Speaker Localization Using Graphical Models
abstract: In this work we propose an approach to combine audio and video modalities for person tracking using graphical models. We demonstrate a principled and intuitive framework for combining these modalities to obtain robustness against occlusion and change in appearance. We further exploit the temporal correlations that exist for a moving object between adjacent frames to account for the cases where having both modalities might still not be enough, e.g., when the person being tracked is occluded and not speaking. Improvement in tracking results is shown at each step and compared with manually annotated ground truth

title: A multi-view probabilistic model for 3D object classes
abstract: We propose a novel probabilistic framework for learning visual models of 3D object categories by combining appearance information and geometric constraints. Objects are represented as a coherent ensemble of parts that are consistent under 3D viewpoint transformations. Each part is a collection of salient image features. A generative framework is used for learning a model that captures the relative position of parts within each of the discretized viewpoints. Contrary to most of the existing mixture of viewpoints models, our model establishes explicit correspondences of parts across different viewpoints of the object class. Given a new image, detection and classification are achieved by determining the position and viewpoint of the model that maximize recognition scores of the candidate objects. Our approach is among the first to propose a generative probabilistic framework for 3D object categorization. We test our algorithm on the detection task and the viewpoint classification task by using “car” category from both the Savarese et al. 2007 and PASCAL VOC 2006 datasets. We show promising results in both the detection and viewpoint classification tasks on these two challenging datasets.

title: Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories
abstract: Current computational approaches to learning visual object categories require thousands of training images, are slow, cannot learn in an incremental manner and cannot incorporate prior information into the learning process. In addition, no algorithm presented in the literature has been tested on more than a handful of object categories. We present an method for learning object categories from just a few training images. It is quick and it uses prior information in a principled way. We test it on a dataset composed of images of objects belonging to 101 widely varied categories. Our proposed method is based on making use of prior information, assembled from (unrelated) object categories which were previously learnt. A generative probabilistic model is used, which represents the shape and appearance of a constellation of features belonging to the object. The parameters of the model are learnt incrementally in a Bayesian manner. Our incremental algorithm is compared experimentally to an earlier batch Bayesian algorithm, as well as to one based on maximum-likelihood. The incremental and batch versions have comparable classification performance on small training sets, but incremental learning is significantly faster, making real-time learning feasible. Both Bayesian methods outperform maximum likelihood on small training sets.

title: Variational Shift Invariant Probabilistic PCA for Face Recognition
abstract: While PCA learns a subspace that captures the variations of the data, it assumes the collected data is well pre-processed (i.e., the pictures for faces are aligned by eye corners), this usually introduces a huge mount of manual labor for human. While people have been developing automatic eye alignment tools for such purpose, detecting eyes with robustness and accuracy is still an open problem for research. We propose to learn PCA while at the same time eliminating the mis-alignment in the data. We formulate the PCA model in a generative framework, and introduce the mis-alignment as a hidden variable in the model. A novel variational message passing (J. Winn and C. Bishop, 2004) update rules is then derived to learn the parameters. The experiments show that the performance of PCA based face recognition is significantly improved by our algorithm when misalignments exist

title: CTH01-3: A Low-complexity Adaptive Subcarrier, Bit, and Power Allocation Algorithm for OFDMA Systems
abstract: Adaptive subcarrier, bit, and power allocation schemes for orthogonal frequency division multiple access (OFDMA) systems are considered. A low-complexity algorithm is proposed to minimize the overall transmit power under certain fairness constraints: Data rate and bit-error-rate (BER) requirements for each user. In the proposed algorithm, subcarrier allocation is first executed, which follows two steps: An initial subcarrier allocation to satisfy the individual data rate requirement; then a residual subcarrier allocation to further reduce the overall transmit power. After that, a single-user optimal bit and power allocation is performed for each user based on the water-filling strategy. The proposed heuristic subcarrier allocation algorithm has linear complexity in the number of users and subcarriers, as opposed to the combinatorial nature of the optimal solutions. More importantly, the proposed resource allocation scheme explores frequency diversity in a more efficient way compared with reference schemes of similar complexity, which makes it more preferable in highly frequency-selective fading channels.