Abstract. In this paper, we tackle the problem of object detection and tracking in a new and challenging domain of wide area surveillance. This problem poses several challenges: large camera motion, strong parallax, large number of moving objects, small number of pixels on target, single channel data and low framerate of video. We propose a method that overcomes these challenges and evaluate it on CLIF dataset. We use median background modeling which requires few frames to obtain a workable model. We remove false detections due to parallax and registration errors using gradient information of the background image. In order to keep complexity of the tracking problem manageable, we divide the scene into grid cells, solve the tracking problem optimally within each cell using bipartite graph matching and then link tracks across cells. Besides tractability, grid cells allow us to define a set of local scene constraints such as road orientation and object context. We use these constraints as part of cost function to solve the tracking problem which allows us to track fast-moving objects in low framerate videos. In addition to that, we manually generated groundtruth for four sequences and performed quantitative evaluation of the proposed algorithm.

Abstract. In this paper we present a novel approach for detection of independently moving foreground objects in non-planar scenes captured by a moving camera. We avoid the traditional assumptions that the stationary background of the scene is planar, or that it can be approximated by dominant single or multiple planes, or that the camera used to capture the video is orthographic. Instead we utilize a multiframe monocular epipolar constraint of camera motion derived for monocular moving cameras defined by an evolving epipolar plane between the moving camera center and 3D scene points. This constraint is parameterized as a polynomial function of time, and unlike repeated computations of inter-frame fundamental matrix, requires the estimation of fewer unknowns, and provides a more consistent separation between moving and static objects for different levels of noise. This constraint allows us to segment out moving objects in a general 3D scene where other approaches fail because their initial assumptions do not hold, and provides a natural way of fusing temporal information across multiple frames. We use a combination of optical flow and particle advection to capture all motion in the video across a number of frames, in the form of particle trajectories. We then apply the derived multi-frame epipolar constraint to these trajectories to determine which trajectories violate it, thus segmenting out the independently moving objects. We show superior results on a number of moving camera sequences observing non-planar scenes, where other methods fail.

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Abstract. In this paper, we propose a method for detecting humans in imagery taken from a UAV. This is a challenging problem due to small number of pixels on target, which makes it more difficult to distinguish people from background clutter, and results in much larger searchspace. We propose a method for human detection based on a number of geometric constraints obtained from the metadata. Specifically, we obtain the orientation of groundplane normal, the orientation of shadows cast by humans in the scene, and the relationship between human heights and the size of their corresponding shadows. In cases when metadata is not available we propose a method for automatically estimating shadow orientation from image data. We utilize the above information in a geometry based shadow, and human blob detector, which provides an initial estimation for locations of humans in the scene. These candidate locations are then classified as either human or clutter using a combination of wavelet features, and a Support Vector Machine. Our method works on a single frame, and unlike motion detection based methods, it bypasses the global motion compensation process, and allows for detection of stationary and slow moving humans, while avoiding the search across the entire image, which makes it more accurate and very fast. We show impressive results on sequences from the VIVID dataset and our own data, and provide comparative analysis.