Mean Shift Object Tracking Algorithm Research Articles

Performance Measurement of Photoelectric Detection and Target Tracking Algorithm

Abstract To solve the unstable problem of target tracking detection system, this paper proposes an improved mean-shift algorithm for object tracking, establishes object tracking processing model;provides the processing algorithm of object tracking. According to the principal of object tracking, papersets up sky background brightness calculation model in photoelectric tracking optical detection area and detection capability calculation model of space object, analyzes the effect of background illumination on the signal to noise ratio(SNR) of photoelectric tracking system, gives the change curve of detection capability based on the exposure time of CCD camera, SNR threshold of photoelectric detection system and dark current of photoelectric detection system. Through calculation and test , paperprovidesthe comparison results of the improved mean-shift and traditional mean-shift, verifies the correctness of the proposed algorithm and calculation models for photoelectric detection capability in target tracking detection system, the results show the improved mean-shiftobject tracking algorithm and detection capability calculation model are correct.

Visual Target Tracking and Relative Navigation for Unmanned Aerial Vehicles in a GPS-Denied Environment

We present a system for the real-time visual relative navigation of a fixed-wing unmanned aerial vehicle in a GPS-denied environment. An extended Kalman filter is used to construct a vision-aided navigation system by fusing the image processing results with barometer and inertial sensor measurements. Using a mean-shift object tracking algorithm, an onboard vision system provides pixel measurements to the navigation filter. The filter is slightly modified to deal with delayed measurements from the vision system. The image processing algorithm and the navigation filter are verified by flight tests. The results show that the proposed aerial system is able to maintain circling around a target without using GPS data.

Robust Object Tracking Based on Modified Mean-Shift Algorithm

In this article, aiming at the flaws in traditional Mean-shift algorithm for object tracking, we improve the traditional algorithm based on the fusion of Grayscale and Gradient using a layered approach. This algorithm can describe the object more exactly by using Grayscale and Gradient dual tracking factors in object tracking; And it makes up the flaws in traditional Mean-shift algorithm for information expression of spatial location by using layered approach and spatial histogram in describing object. Experimental results show that modified Mean-shift algorithm is superior to traditional Mean-shift algorithm in object tracking with better Robustness and precision, and it can suit deformation, spin, interference, shield and more complex tracking problems.

Video Tracking of Human with Occlusion Based on MeanShift and Kalman Filter

In order to accomplish tracking of moving objects requirements, and overcome the defect of occlusion in the process of tracking moving object, this paper presents a method which uses a combination of MeanShift and Kalman filter algorithm. MeanShift object tracking algorithm uses a histogram to describe the color characteristics of an object, and search the location of an image region that the color histogram is closest to the histogram of the object. Histogram similarity is defined in terms of the Bhattacharya coefficient. When the moving object is a large area blocked, the future state of moving object is estimated by Kalman filter. Experimental results verify that the proposed algorithm achieves efficient tracking of moving objects under the confusing situations.

Mean-Shift Object Tracking with Discrete and Real AdaBoost Techniques

An online mean-shift object tracking algorithm, which consists of a learning stage and an estimation stage, is proposed in this work. The learning stage selects the features for tracking, and the estimation stage composes a likelihood image and applies the mean shift algorithm to it to track an object. The tracking performance depends on the quality of the likelihood image. We propose two schemes to generate and integrate likelihood images: one based on the discrete AdaBoost (DAB) and the other based on the real AdaBoost (RAB). The DAB scheme uses tuned feature values, whereas RAB estimates class probabilities, to select the features and generate the likelihood images. Experiment results show that the proposed algorithm provides more accurate and reliable tracking results than the conventional mean shift tracking algorithms.

A spatial-color mean-shift object tracking algorithm with scale and orientation estimation

In this paper, an enhanced mean-shift tracking algorithm using joint spatial-color feature and a novel similarity measure function is proposed. The target image is modeled with the kernel density estimation and new similarity measure functions are developed using the expectation of the estimated kernel density. With these new similarity measure functions, two similarity-based mean-shift tracking algorithms are derived. To enhance the robustness, the weighted-background information is added into the proposed tracking algorithm. Further, to cope with the object deformation problem, the principal components of the variance matrix are computed to update the orientation of the tracking object, and corresponding eigenvalues are used to monitor the scale of the object. The experimental results show that the new similarity-based tracking algorithms can be implemented in real-time and are able to track the moving object with an automatic update of the orientation and scale changes.