Robust Object Tracking via Improved Mean-Shift Model

Real time and accrual object tracking is ultimate aim of a visual tracker. Recently mean shift object tracking algorithm is studied in an all-round way and utilized to visual tracking in both civil and military fields. Compared to the traditional mean shift algorithm and its other versions distance weight mean shift algorithm can effectively improve the precision and robustness. It analyzes the dissimilarity of target feature color histogram with distance weight and candidate area feature color histogram with distance weight to calculate mean shift vector, and searches for target area via each iteration in candidate area. Though improved on basis of traditional mean shift algorithm, there are a large amount of calculations and the processing speed has already been a bottleneck in real time object tracking. Graphics processor unit (GPU) has special hardware parallel computing resources and has been already employed in various embedded systems. It provides us with an alternative implementation on GPU which can solve such bottleneck problem. The paper proposes a way of implementation of distance weight mean shift algorithm on GPU. The experiment results show that the execution performance of the implementation on GPU is over 56 times speedup of that completely on CPU at most, and can meet the requirement of real time and accuracy.

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.

In order to solve the problems of motion blur and fast motion, a new robust object tracking algorithm using the Kernelized Correlation Filters (KCF) and the Mean Shift (MS) algorithm, called KCFMS is presented in this paper. The object tracking process can be described as: First, we give the initial position and size of the object and use the Mean Shift algorithm to obtain the position of the object. Second, the Kernelized Correlation Filtering algorithm is used to obtain the position of the object in the same frame. Third, we use the cross update strategy to update the object models. In order to improve the tracking speed as much as possible, our object tracking algorithm works only over one layer. This hybrid algorithm has a good tracking effect on the target fast motion and motion blur. We present extensive experimental results on a number of challenging sequences in terms of efficiency, accuracy and robustness.

Nowadays object tracking is a challenge in the fields of computer vision, many algorithms are put forward to overcome the issues such as noisy, colored background, and occlusion of object and also illusion which is low and sudden changes in real-time videos, trackers cannot perform well in real-time environment. An efficient algorithm is needed for effective tracing of an object because of variation in position under various conditions. Here, the MS algorithm is used due to its efficiency and performance. MS tracking algorithm is used to obtain the position of an object by using a Kalman filter, therefore, the performance of tracking an object for different videos is evaluated but it does not improve the target localization. The advanced version is developed for tracking and representing the object in different video sequences are MS and CBWH MS using Kalman filter. Therefore, the proposed method is better than the traditional MS track, which results in the constant tracking of an object throughout the video. It does not get influenced by occlusions and less subjected to background cluttering.

In this paper, we improve the real-time object tracking algorithm of Yang [1] which uses a symmetric similarity function between spatially smoothed kernel-density estimates of the model and the target distributions. This spatial smoothed process applied on the centre points of the probability density functions increases not only computational complexity but also noise sensitivity. After reducing background information and using a new simple similarity function between the model and the target distributions, the proposed algorithm successfully coped with camera motion, partial occlusions and clutter with lower computational complexity. For tracking object, the mean shift algorithm is used iteratively. The simplicity of the new similarity function leads to an efficient and robust nonparametric tracking algorithm. The mathematical results obtained about the performance of the proposed algorithm on several image sequences are represented comparatively in a tabular format.

Object detection and tracking under complex environment are challenging because of the disturbances induced by background clutter, illumination changes, occlusions and other factors. The bulk of traditional algorithms basically rely on hand‐crafted features, which are not sufficiently robust to a complex environment. Moreover, the processes of detection and tracking are separated, which leads to the overall efficiency not high. In this study, a novel local probability model (LPM)‐based mean shift (MS) algorithm is proposed to integrate object detection and tracking. The main contributions include: (i) a new framework based on the combination of LPM and MS is established for the integration of object tracking and detection. (ii) For object detection, the training and prediction of LPM are built by stacked denoising autoencoders based deep learning. (iii) For object tracking, an MS tracking algorithm leveraging LPM is modified to improve the tracking efficiency under a complex environment. Experimental results demonstrate that the proposed method is superior to the colour histograms based MS and histograms of oriented gradients based MS in terms of robustness and tracking accuracy.

A mean shift algorithm has gained special attention in recent years due to its simplicity to enable real-time tracking. However, the traditional mean shift tracking algorithm can fail to track target under occlusions. In this paper we propose a novel technique which alleviates the limitation of mean shift tracking. Our algorithm employs the Kalman filter to estimate the target dynamics information. Moreover, the proposed algorithm performs the background check process to calculate the similarity which expresses how similar to target the background is. We then find the exact target position combining the motion estimation by Kalman filter and the color based estimation by the mean shift algorithm based on the similarity value. Therefore, the proposed algorithm can robustly track targets under several types of occlusion, while the mean shift and mean shift-Kalman filter algorithms fail.

Many tracking algorithms have difficulties dealing with occlusions and background clutters, and consequently don't converge to an appropriate solution. Tracking based on the mean shift algorithm has shown robust performance in many circumstances but still fails e.g. when encountering dramatic intensity or colour changes in a pre-defined neighbour hood. In this paper, we present a robust tracking algorithm that integrates the advantages of mean shift tracking with those of tracking local invariant features. These features are integrated into the mean shift formulation so that tracking is performed based both on mean shift and feature probability distributions, coupled with an expectation maximisation scheme. Experimental results show robust tracking performance on a series of complicated real image sequences.

A registration-based tracking algorithm based on noise separation

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.

The mean shift algorithm has achieved considerable success in object tracking due to its simplicity and robustness. It finds local minima of a similarity measure between the color histograms or kernel density estimates of the model and target image. The most typically used similarity measures are the Bhattacharyya coefficient or the Kullback-Leibler divergence. In practice, these approaches face three difficulties. First, the spatial information of the target is lost when the color histogram is employed, which precludes the application of more elaborate motion models. Second, the classical similarity measures are not very discriminative. Third, the sample-based classical similarity measures require a calculation that is quadratic in the number of samples, making real-time performance difficult. To deal with these difficulties we propose a new, simple-to-compute and more discriminative similarity measure in spatial-feature spaces. The new similarity measure allows the mean shift algorithm to track more general motion models in an integrated way. To reduce the complexity of the computation to linear order we employ the recently proposed improved fast Gauss transform. This leads to a very efficient and robust nonparametric spatial-feature tracking algorithm. The algorithm is tested on several image sequences and shown to achieve robust and reliable frame-rate tracking.

In traditional Mean Shift algorithm, color histogram is usually used as the features vectors and the dissimilarity between the referenced targets and the target candidates is expressed by the metric derived from the Bhattacharyya coefficients. The traditional Mean Shift procedure is used to find the real position of the target by looking for the regional minimum of the distance function iteratively. While the target's color is similar to the background, the algorithm will miss the target. This paper presents a new mean shift algorithm based on spatial edge orientation histograms, using space distribution and texture information as matching information. Meanwhile a Kalman filter will be used to predict the target's position. Experimental results demonstrate that the proposed algorithm can deal with intricate conditions, such as significant clutter, partial occlusions, and it can track objects efficiently and robustly.

Object tracking in real-world scenarios is often hampered by the simultaneous challenges of low light and partial object occlusion. While existing evaluation datasets have tackled these scenarios separately-focusing either on low-light settings or amodal perception-their co-occurrence has rarely been studied. To bridge this gap, we introduce LAOT (Low-Light Amodal Object Tracking), a benchmark tailored to assess how state-of-the-art tracking algorithms perform under the combined challenges of dim lighting and partial object occlusion. The dataset consists of 201 diverse video sequences with over 16K frames, each meticulously annotated with both modal bounding boxes (representing the visible part of the object) and amodal bounding boxes (estimating the complete object extent, including occluded regions). To characterize occlusion severity, object instances are categorized into three levels based on visible proportion: 0-20% (no occlusion), 20-80% (partial occlusion), and 80-100% (heavy occlusion). We benchmark 21 state-of-the-art tracking algorithms, including ARTrack [1], AVTrack [2], DropTrack [3], SeqTrack [4], ProContEXT [5], and HIPTrack [6], using Average Precision (AP) as the evaluation metric across occlusion levels. The results reveal a consistent decline in tracking accuracy as occlusion increases. For example, DropTrack achieves 0.8382 AP without occlusion but drops to 0.1730 AP with heavy occlusion; similarly, AVTrack falls from 0.7375 to 0.1276. These findings expose the limitations of current methods in coping with simultaneous challenges in low light and amodal perception. LAOT serves as a comprehensive and diagnostic benchmark to guide the development of robust perception-aware tracking algorithms suited for visually degraded environments.The LAOT is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/LSW-CVLab/LAOT</uri>

Mean shift algorithm has grained great success in object tracking domain due to its ease of implementation, real time response and robust tracking performance, however, the fixed kernel bandwidth may cause tracking failure for size changing objects. A novel object tracking algorithm for FLIR imagery is proposed based on mean shift with adaptive bandwidth. The scale invariant feature transform is employed to compute the affine model between the successive frames. Then, the scale and orientation of the kernel can be estimated by the gained parameters. Experiment results verify the effectives and robustness of this extraction algorithm which can improve the tracking performance efficiently.

Object tracking is an important technology in video surveillance. The main approach is Mean Shift algorithm and its improved version. Studies show that the traditional Mean Shift algorithm adopts a fixed searching window in the tracking process, which cannot adjust the template adaptively. The improved algorithm, CamShift, overcomes this problem with an adaptively changing searching window. However, these algorithms are both based on color tracking, which requires that the colors of the foreground targets are unique. If the color of the target is similar to the color of the background, tracking errors will occur or tracking targets will be lost. In this study, we developed an adaptive gradient enhanced texture based tracking algorithm for traffic monitoring applications. This algorithm combines the characteristics of the color and texture of objects. The algorithm builds a joint histogram template of color and texture for targeting, which solves the problems of tracking targets losing when the color of the object is similar to the color of the background. The experiments show that the algorithm can improve the accuracy and robustness of object tracking.