Techniques For Video Surveillance: Automatic Video Editing And Target Tracking

Abstract

Typical video surveillance control rooms include a collection of monitors connected to a large camera network, with many fewer operators than monitors. The cameras are usually cycled through the monitors, with provisions for manual over-ride to display a camera of interest. In addition, cameras are often provided with pan, tilt and zoom capabilities to capture objects of interest. In this dissertation, we develop novel ways to control the limited resources by focusing them into acquiring and visualizing the critical information contained in the surveyed scenes. First, we consider the problem of cropping surveillance videos. This process chooses a trajectory that a small sub-window can take through the video, selecting the most important parts of the video for display on a smaller monitor area. We model the information content of the video simply, by whether the image changes at each pixel. Then we show that we can find the globally optimal trajectory for a cropping window by using a shortest path algorithm. In practice, we can speed up this process without affecting the results, by stitching together trajectories computed over short intervals. This also reduces system latency. We then show that we can use a second shortest path formulation to find good cuts from one trajectory to another, improving coverage of interesting events in the video. We describe additional techniques to improve the quality and efficiency of the algorithm, and show results on surveillance videos. Second, we turn our attention to the problem of tracking multiple agents moving amongst obstacles, using multiple cameras. Given an environment with obstacles, and many people moving through it, we construct a separate narrow field of view video for as many people as possible, by stitching together video segments from multiple cameras over time. We employ a novel approach to assign cameras to people as a function of time, with camera switches when needed. The problem is modeled as a bipartite graph and the solution corresponds to a maximum matching. As people move, the solution is efficiently updated by computing an augmenting path rather than by solving for a new matching. This reduces computation time by an order of magnitude. In addition, solving for the shortest augmenting path minimizes the number of camera switches at each update. When not all people can be covered by the available cameras, we cluster as many people as possible into small groups, then assign cameras to groups using a minimum cost matching algorithm. We test our method using numerous runs from different simulators. Third, we relax the restriction of using fixed cameras in tracking agents. In particular, we study the problem of maintaining a good view of an agent moving amongst obstacles by a moving camera, possibly fixed to a pursuing robot. This is known as a two-player pursuit evasion game. Using a mesh discretization of the environment, we develop an algorithm that determines, given initial positions of both pursuer and evader, if the evader can take any moving strategy to go out of sight of the pursuer, and thus win the game. If it is decided that there is no winning strategy for the evader, we also compute a pursuer's trajectory that keeps the evader within sight, for every trajectory that the evader can take. We study the effect of varying the mesh size on both the efficiency and accuracy of our algorithm. Finally, we show some earlier work that has been done in the domain of anomaly detection. Based on modeling co-occurrence statistics of moving objects in time and space, experiments are described on synthetic data, in which time intervals and locations of unusual activity are identified.