We describe an approach to segmenting foreground regions corresponding to a group of people into individual humans. Given background subtraction and ground plane homography, hierarchical parttemplate matching is employed to determine a reliable set of human detection hypotheses, and progressive greedy optimization is performed to estimate the best configuration of humans under a Bayesian MAP fr...

The detection of moving object is one of the key techniques for video surveillance. In order to extract the moving object robustly in complex background, this paper presents a novel background subtraction method for detecting foreground objects in dynamic scenes. The difference image of color distance between current image and the reference background image in YUV color space is first obtained....

Background subtraction is an important preprocessing step in many modern Computer Vision systems. Much work has been done especially in the field of color image based foreground segmentation. But the task is not an easy one so, state of the art background subtraction algorithms are complex both in programming logic and in run time. Depth cameras might offer a compelling alternative to those app...

In this paper, we present a grid-based tracking by detection methodology, applied to 3D people tracking for multi-camera video surveillance. In particular, frame-by-frame detection is performed by means of hierarchical likelihood grids, using edge matching through the oriented distance transform on each camera view and a simple person model, followed by likelihood grids clustering in state-spac...

Low-cost systems that can obtain a high-quality foreground segmentation almost independently of the existing illumination conditions for indoor environments are very desirable, especially for security and surveillance applications. In this paper, a novel foreground segmentation algorithm that uses only a Kinect depth sensor is proposed to satisfy the aforementioned system characteristics. This ...

In this paper, we present MAPTrack a robust tracking framework that uses a probabilistic scheme to combine a motion model of an object with that of its appearance and an estimation of its position. The motion of the object is modelled using the Gaussian Mixture Background Subtraction algorithm, the appearance of the tracked object is enumerated using a color histogram and the projected location...

In this paper, detection and tracking of moving object with shadow removal, automated counting and alarm generation was proposed. Improved Gaussian mixture model was used for background subtraction and modeling as it robustly deals with lighting changes, repetitive motions, clutter and slowly moving objects. Shadow detection and removal was performed using improved HSV color space method. Morph...