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This paper presents an improved pedestrian tracking algorithm with image sequences acquired by surveillance cameras. This pedestrian tracking algorithm is based on mean shift algorithm, and it uses color histogram equalization to improve the original algorithm. The improved algorithm performs much better than the original algorithm in some situations. We use the CAVIAR project/IST 2001 37540 dataset...
The mean shift tracker has achieved great success in visual object tracking due to its efficiency being nonparametric. However, it is still difficult for the tracker to handle scale changes of the object. In this paper, we associate a scale adaptive approach with the mean shift tracker. Firstly, the target in the current frame is located by the mean shift tracker. Then, a feature point matching procedure...
This paper proposes a method to track abnormalities in successive frames in a capsule endoscopic image sequence. Exact tracking of an abnormality in the gastrointestinal tract is useful in preparing the content for educational systems. However, if the abnormality is de-formable over continuous frames and its features are not highly distinct, it is difficult to track abnormalities precisely. The proposed...
The process required to track cellular structures is a key task in the study of cell migration. This allows the accurate estimation of motility indicators that help in the understanding of mechanisms behind various biological processes. This paper reports a particle-based fully automatic tracking framework that is able to quantify the motility of living cells in time-lapse images. Contrary to the...
This paper presents a new approach which combines the Kernel Density Estimation and Trust Region algorithm for tracking objects in video sequences. Kernel density estimation (KDE) of the object's color distribution is built from the object region and used to generate a probability map for each incoming frame. Tracking is accomplished by localizing blobs in the maps. Compared with color histograms...
In this paper, we address the problem of vehicle detection and tracking with low-angle cameras by combining windshield detection and feature points clustering, effectively fusing several primitive image features such as color, edge and interest point. By exploring various heterogenous features and multiple vehicle models, we achieve at least two improvements over the existing methods: higher detection...
This paper proposes a framework to track faces in color video sequences. The Adaptive Support Vector Tracker (ASVT) combines face detection with target tracking through using an adaptive filter in unconstrained videos. The adjacent locations of the target point are predicted in a search window reducing the number of image regions that are candidates for faces. Thus, the method can predict the object...
In this paper an adaptive Gaussian mixture model is introduced firstly to remove the shadow of regions of interest in the detection of moving human body from current video sequences. Then use a proposed method of obtaining ROI. From the view of the tracking effect, it can be concluded that this method of removal shadow of regions of interest can improve the precision rate of segment of moving people...
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 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...
The main objective of this paper is to develop multiple human object tracking approach based on motion estimation and detection, background subtraction, shadow removal and occlusion detection. A reference frame is initially used and considered as background information. While a new object enters into the frame, the foreground information and background information are identified using the reference...
This paper presents a self-adapting algorithm based on Mean Shift model to track the target in video sequences. Firstly, two-dimensional histogram is used to represent the target instead of one-dimensional histogram, so as to better distinguish the target from background. Secondly, algorithm has been improved by adding self-adapting progress to remove errors caused by local maximum. Experiments on...
We present a real-time distributed system for tracking with non-overlapping camera views. Each camera performs multi-object tracking, and cameras communicate with each other in a peer-to-peer manner for consistent labeling. To match objects across non-overlapping views, we employ multiple features, namely color histogram, height, travel time and speed. First, camera configuration and reference values...
This paper proposes an extension to the mean shift tracking by using XY projection-histograms to model the object. More than providing statistical information about the target to track, they embed information about the spatial arrangement of pixels. This approach, without any complexity increase, provides a better robustness and quality of the tracking. That is asserted by the experiments performed...
Maintaining the stability of tracks on multiple targets in video over extended time periods remains a challenging problem. A few methods which have recently shown encouraging results in this direction rely on learning context models or the availability of training data. However, this may not be feasible in many application scenarios. Moreover, tracking methods should be able to work across multiple...
The research for metadata extraction originates from the intelligent video surveillance system, which is widely used in outdoor and indoor environment for the aims of traffic monitor, security guard, and intelligent robot. Various features are extracted from the surveillance image sequences such as target detection, target tracking, object's shape and activities. However, the trend of more and more...
This paper presents an algorithm that detects and tracks marine vessels in video taken by a nonstationary camera installed on an untethered buoy. The video is characterized by large inter-frame motion of the camera, cluttered background, and presence of compression artifacts. Our approach performs segmentation of ships in individual frames processed with a color-gradient filter. The threshold selection...
Forewarning to avoid potential traffic accidents is of great importance for Intelligent Transportation Systems (ITS). Under pedestrian and vehicle mixed traffic conditions like urban road intersections, traffic monitoring and forewarning have especially important values. Therefore in this paper a novel urban traffic information analysis and forewarning system is presented. Our system contains modules...
The particle filtering technique with multiple cues is a powerful technique for tracking objects in image sequences. In this paper, we proposed a novel particle filter which embeds the Mean Shift optimization and a data association technique based on the joint probabilistic data association (JPDA). We use the adaptive mixture of color and texture cues to represent the distributions of the target....
In this paper, we propose a novel approach that combines particle filter tracking and 3D graph cut based segmentation to achieve silhouette tracking against drastic scale change and occlusion. The segmentation module offers particle filter tracking procedure the target shape information to compensate spatial information loss in the histogram based particle filter tracking process. Meanwhile, particle...
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