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The concept of active tracking is presented to simulate the characteristics of human vision in intelligent visual surveillance. The Pan/Tilt/Zoom (PTZ) camera is generally used for active tracking. In this paper, we present a novel and effective approach for active object tracking with a PTZ camera, and construct a near real-time system for indoor and outdoor scenes. The tracking algorithm of our...
Many computer vision algorithms for pattern matching, object tracking, and 3-D reconstruction, etc., begin with feature detection and matching. Common feature detectors such as Harris, Sobel, Canny, and Difference of Gaussians perform basic linear algebra operations on an image in order to identify "corners" or "edges" for matching. These detectors however, require single-channel...
In the field of nanotechnology, tracking freely swimming microorganisms under a microscope is difficult. An image of the target often rotates and changes shape as it moves. Further, the difficulty increases with background changes and fluctuations of colors and the brightness because of differences in the surrounding environment. To address these problems, we propose a tracking method combined with...
Particle filter is a popular stochastic tracker for object tracking. In this paper, we proposed a novel algorithm for object tracking based on particle filter and Scale Invariant Feature Transform (SIFT). The result of SIFT matching does not adopt to reweight the particles as previous methods, we adopts a hybrid schema to supplement the particle distribution of traditional factor sampling with importance...
This paper proposes a position-free interface system that enables a user to input and obtain information everywhere in the environment with a projector-camera system. Hand-waving detected in the image is used as a cue to start the system, to distinguish gestures based on the user's intension of operation from other daily-life motions, and to specify the position of the user's hand. Hand-waving detection...
The paper presents a new tracking scheme based on the object-strips color (OSC) feature. Firstly, the images captured by the camera are transformed into a format which is suitable for object tracking. Secondly, background subtraction method is used to detect the moving object. Then the OSC feature is represented by dividing the detected object into several strips and integrating the mean hue of each...
This paper presents a real-time and automatic video tracking system with a single pan-tilt-zoom (PTZ) camera. Compared with fixed camera, it can enlarge the surveillance area. By using three-dimensional background-weighted histogram in HSV color space, an improved mean-shift algorithm is proposed, and the algorithm can track target with multiple colors in real time. To keep the target in the center...
In this paper we propose a computationally efficient scale adaptive tracking method using a hybrid color histogram matching scheme. Firstly, we report an important property of the Chi-squared measure- It outperforms Bhattacharyya measure in the task of histogram matching from a few significantly similar multimodal histograms. Also, Bhattacharyya measure performs better while selecting matches from...
This paper proposes a new target matching method for multiple cameras based on texture energy. The target region is represented by texture energy instead of traditional color features. In our method, the frame-difference is used in multi-target detecting, and the kalman filter is utilized in multi-target tracking. None camera calibrations are required in the new method, neither the constraint that...
In this paper, multiple cameras are used to implement a multi-purpose real-time visual tracking system. A modified adaptive background subtraction method is used to detect moving objects, and a multi-cue matching approach is employed to track a moving target. The epipolar constraint and color histogram matching are used to deal with the correspondence problem that occurs when multiple cameras are...
In this paper, we present a local graph matching based method for tracking cells and cell divisions in noisy images. We work with plant cells, where the cells are tightly clustered in space and computing correspondences across time can be very challenging. The local graph matching method is able to track the cells and cell divisions even when significant portions of the images are corrupted due to...
In this paper, we propose an effective approach for tracking distribution of objects. The approach uses a competition between a tracked objet and background distributions using active contours. Only the segmentation of the object in the first frame is required for initialization. We evolve the object contour by assigning pixels in a fashion that maximizes the likelihood of the object versus the background...
Visual tracking algorithm plays an important role in the fields such as guidance and surveillance of mobile robot. These applications require the vision algorithm with strong robustness and fast processing speed. Camshift uses color histogram as a characteristic and mean shift as the search algorithm. The direction of grads ascension is used to reduce the characteristic match time, so that the object...
Matching based on local brightness is quite limited, because small changes on local appearance invalidate the constancy in brightness. The root of this limitation is its treatment regardless of the information from the spatial contexts. This papers leaps from brightness constancy to context constancy, and thus from optical flow to contextual flow. It presents a new approach that incorporates contexts...
Most of the reported object tracking methods achieved by estimating the object position cannot suit for large image deformations. For this reason, a new object tracking base on MCMC point matching method is presented in this paper. This method applies MCMC algorithm to solve the posterior probability distribution problem and obtain the optimal matching parameters include position, rotation, scale...
Aiming at improving the performance of non-rigid object tracking in video sequences acquired by a stationary camera, an effective method based on the adaptive color segmentation and object part model was presented. In this work, we modeled background and obtained the foreground blobs with an effective adaptive background updating method based on Gaussian mixture model (GMM), and then the regions in...
This paper presents a novel an adaptive background subtraction method to segment the moving regions and locate the positions of human bodies. Most methods proposed so far adjust the permissible range of the background image variations according to the training samples of background images. Thus, the detection sensitivity decreases at those pixels having wide permissible ranges. If we can narrow the...
This paper describes a prototype system for performing handover between cameras with non-overlapping views. The design is being used to identify problems that may arise in the development of a larger, more capable, and fully automatic system. If there is no information about the spatio-temporal relationship between cameras to assist in matching individuals, similarities in appearance may be used....
This paper presents a tracking algorithm based on a sequential importance sampling (SIS) particle filter scheme followed by a resampling strategy where shape and color cues are exploited to handle deformable objects. The state vector is composed by a set of corners and it enables to jointly describe position and shape of the target. Mean Shift trackers, applied to color cues associated to state subspaces,...
Robust and real time moving object tracking is a tricky job in computer vision problems. Particle filtering has been proven very successful for non-Gaussian and non-linear estimation problems. In this paper, we first try to develop a color based particle filter. In this approach, the object tracking system relies on the deterministic search of window, whose color content matches a reference histogram...
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