Abstract. The sparse representation has been widely used in many areas and utilized for visual tracking. Tracking with sparse representation is formulated as searching for samples with minimal reconstruction errors from learned template subspace. However, the computational cost makes it unsuitable to utilize high dimensional advanced features which are often important for robust tracking under dynamic environment. Based on the observations that a target can be reconstructed from several templates, and only some of the features with discriminative power are significant to separate the target from the background, we propose a novel online tracking algorithm with two stage sparse optimization to jointly minimize the target reconstruction error and maximize the discriminative power. As the target template and discriminative features usually have temporal and spatial relationship, dynamic group sparsity (DGS) is utilized in our algorithm. The proposed method is compared with three state-of-art trackers using five public challenging sequences, which exhibit appearance changes, heavy occlusions, and pose variations. Our algorithm is shown to outperform these methods. 1

Developing computer game agents is often a lengthy and expensive undertaking. Detailed domain knowledge and decision-making procedures must be encoded into the agent to achieve realistic behavior. In this paper, we simplify this process by using the ICARUS cognitive architecture to construct game agents. The system acquires structured, high fidelity methods for agents that utilize a vocabulary of concepts familiar to game experts. We demonstrate our approach by first acquiring behaviors for football agents from video footage of college football games, and then applying the agents in a football simulator.

We present a new paradigm for tracking objects in video in the presence of other similar objects. This branch-andtrack paradigm is also useful in the absence of motion, for the discovery of repetitive patterns in images. The object of interest is the lead object and the distracters are extras. The lead tracker branches out trackers for extras when they are detected, and all trackers share a common set of features. Sometimes, extras are tracked because they are of interest in their own right. In other cases, and perhaps more importantly, tracking extras makes tracking the lead nimbler and more robust, both because shared features provide a richer object model, and because tracking extras accounts for sources of confusion explicitly. Sharing features also makes joint tracking less expensive, and coordinating tracking across lead and extras allows optimizing window positions jointly rather than separately, for better results. The joint tracking of both lead and extras can be solved optimally by dynamic programming and branching is quickly determined by efficient subwindow search. Matlab experiments show near real time performance at 5-30 frames per second on a single-core laptop for 240 by 320 images. Figure 1. Top row: from left to right are the 1 st, 100 th and 150 th frame of a video sequence. The target to be tracked is bounded by a red rectangle in the first frame. Bottom row: Three possible tracking outcomes for the 200 st frame. The appearance change of the birds are dramatic and the confusion is most significant in the 150 th frame: The second bird resembles the first bird in frame 100 more than the first bird in frame 150 resembles its own image in frame 100. As a result of this confusion, a typical tracker can easily follow the wrong target (bottom left), or encompass both birds (bottom center) if the window is allowed to change shape. In contrast, our tracker avoids this confusion (bottom right) and tracks each bird correctly. In addition, each tracker adapts better to change, because trackers share features. (All demo videos used in this paper are downloaded from YouTube.) 1.

Abstract. We present a method for tracking people in monocular broadcast sports videos by coupling a particle filter with a vote-based confidence map of athletes, appearance features and optical flow for motion estimation. The confidence map provides a continuous estimate of possible target locations in each frame and outperforms tracking with discrete target detections. We demonstrate the tracker on sports videos, tracking fast and articulated movements of athletes such as divers and gymnasts and on non-sports videos, tracking pedestrians in a PETS2009 sequence. 1

Abstract. Using domain knowledge to speed up learning is widely accepted but theory revision of such knowledge continues to use general syntactic operators. Using such operators for theory revision of teleoreactive logic programs is especially expensive in which proof of a top-level goal involves playing a game. In such contexts, one should have the option to complement general theory revision with domain-specific knowledge. Using American football as an example, we use Icarus ’ multi-agent teleoreactive logic programming ability to encode a coach agent which infers faults during a game and at its conclusion applies procedural attachments to fix programs of the other agents. Our results show effective learning using as few as twenty examples. We also show that structural changes made by such revision can produce performance gains that cannot be matched by doing only numeric optimization. 1

Developing autonomous agents for computer games is often a lengthy and expensive undertaking that requires manual encoding of detailed and complex knowledge. In this paper we show how to acquire hierarchical skills for controlling a team of simulated football players by observing video of college football play. We then demonstrate the results in the Rush 2008 football simulator, showing that the learned skills have high fidelity with respect to the observed video and are robust to changes in the environment. Finally, we conclude with discussions of this work and of possible improvements. 1

Motion synchrony, i.e., the coordinated motion of a group of individuals, is an interesting phenomenon in nature or daily life. Fish swim in schools, birds fly in flocks, soldiers march in platoons, etc. Our goal is to detect motion synchrony that may be present in the video data, and to track the group of moving objects as a whole. This opens the door to novel algorithms and applications. To this end, we model individual motions as video tubes in space-time, define motion synchrony by the geometric relation among video tubes, and track a whole set of tubes by dynamic programming. The resulting algorithm is highly efficient in practice. Given a video clip of T frames of resolution X × Y, we show that finding the K spatially correlated video tubes and determining the presence of synchrony can be solved optimally in O(XY T K) time. Preliminary experiments show that our method is both effective and efficient. Typical running times are 30 − 100 VGA-resolution frames per second after feature extraction, and the accuracy for the detection of synchrony is more than 90 % as evaluated in our annotated data set.