Integrating region and boundary information for spatiallycoherent object tracking

Chung, Desmond; MacLean, W. James; Dickinson, Sven

doi:10.1016/j.imavis.2005.09.020

Cited by 6 publications

(3 citation statements)

References 35 publications

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“…However, they assume a static background model to facilitate automatic contour initialization and better tracking. A similar method is proposed by Chung et al [28], who employ the EM framework to iterate between region motion estimation and segmentation using active contours, but unlike [27] do not rely on a static background. Cremers and Soatto [29] propose a more holistic approach and treat the spatiotemporal volume as a single entity instead of working with pairs of frames.…”

Section: Related Workmentioning

confidence: 99%

Spatiotemporal Closure

Levinshtein

Sminchisescu

Dickinson

2011

Computer Vision – ACCV 2010

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Abstract. Spatiotemporal segmentation is an essential task for video analysis. The strong interconnection between finding an object's spatial support and finding its motion characteristics makes the problem particularly challenging. Motivated by closure detection techniques in 2D images, this paper introduces the concept of spatiotemporal closure. Treating the spatiotemporal volume as a single entity, we extract contiguous "tubes" whose overall surface is supported by strong appearance and motion discontinuties. Formulating our closure cost over a graph of spatiotemporal superpixels, we show how it can be globally minimized using the parametric maxflow framework in an efficient manner. The resulting approach automatically recovers coherent spatiotemporal components, corresponding to objects, object parts, and object unions, providing a good set of multiscale spatiotemporal hypotheses for high-level video analysis.

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Section: Related Workmentioning

confidence: 99%

Spatiotemporal Closure

Levinshtein

Sminchisescu

Dickinson

2011

Computer Vision – ACCV 2010

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“…In [39], Chung et al combined region-based and boundary-based techniques on optical flow to perform spatially coherent object tracking. Their approach uses feed-forward inside frame-processing steps (region-based information to boundary-based computations) and feedback between subsequent frames.…”

Section: Segmentation Of Optical Flowmentioning

confidence: 99%

Locating moving objects in car-driving sequences

et al. 2014

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This paper presents a system for the search and detection of moving objects in a sequence of images previously captured by a camera installed in a conventional vehicle. The objective is the design and implementation of a software system based on optical flow analysis to detect and identify moving objects as perceived by a driver, taking into account that these objects could interfere with the driver's behavior, either through distraction or by posing an actual danger that may require an active response. The problem presents significant difficulties because the vehicle travels on conventional roads, open to normal traffic. Consequently, the scenes are recorded with natural lighting, i.e., under highly variable conditions (intensity, shadows, etc.). Furthermore, the use of a moving camera makes it difficult to properly identify static objects such as the road itself, signals, buildings, landscapes, and moving objects of the same speed, such as pedestrians or other vehicles. The proposed method consists of three stages. First, the optical flow is calculated for each image of the sequence, as a first estimate of the apparent motion. In a second step, two segmentation processes are addressed: the optical flow itself and the images of the sequence. Finally, in the last stage, the results of these two segmentation processes are combined to obtain the movement of the objects present in the sequence, identifying both their direction and magnitude. The quality of the results obtained with different sequences of real images makes this software suitable for systems to study driver behavior and to help detect danger situations, as various international traffic agencies consider in their research projects.

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“…where (û k ,v k ) are the estimated control parameters computed using equation (12), and N is the total number of points of the object. The minimization entails solving a system of equations given by ∂E/∂d x = 0, which, in turn, can be represented by equation Ad x = b x , where A is given by:…”

Section: Velocity Field Approximation Using Bspline Surfacesmentioning

confidence: 99%

Tracking of Multiple Objects Using Optical Flow Based Multiscale Elastic Matching

Luo

Bhandarkar

Dynamical Vision

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A novel hybrid region-based and contour-based multiple object tracking model using optical flow based elastic matching is proposed. The proposed elastic matching model is general in two significant ways. First, it is suitable for tracking of both, rigid and deformable objects. Second, it is suitable for tracking using both, fixed cameras and moving cameras since the model does not rely on background subtraction. The elastic matching algorithm exploits both, the spectral features and contour-based features of the tracked objects, making it more robust and general in the context of object tracking. The proposed elastic matching algorithm uses a multiscale optical flow technique to compute the velocity field. This prevents the multiscale elastic matching algorithm from being trapped in a local optimum unlike conventional elastic matching algorithms that use a heuristic search procedure in the matching process. The proposed elastic matching based tracking framework is combined with Kalman filter in our current experiments. The multiscale elastic matching algorithm is used to compute the velocity field which is then approximated using Bspline surfaces. The control points of the B-spline surfaces are used directly as the tracking variables in a Kalman filtering model. The B-spline approximation of the velocity field is used to update the spectral features of the tracked objects in the Kalman filter model. The dynamic nature of these spectral features are subsequently used to reason about occlusion. Experimental results on tracking of multiple objects in real-time video are presented.

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Integrating region and boundary information for spatiallycoherent object tracking

Cited by 6 publications

References 35 publications

Spatiotemporal Closure

Spatiotemporal Closure

Locating moving objects in car-driving sequences

Tracking of Multiple Objects Using Optical Flow Based Multiscale Elastic Matching

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