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We present in this paper a novel framework for multiple object tracking. The proposed algorithm consists of two main steps. The first one initializes the correspondences between detected objects and tracks by using the spatio-colorimetric model of tracks coupled with the appearance-based descriptors of objects. Two color-based models are used to enhance the robustness of the tracking result. In the...
The paper proposes an intelligent robotic system which is able to be (re)configured, at demand, for two deployment scenarios. a) The first task is to move the platform after a trajectory determined by the direction to a fixed point and avoid any obstacles occurring in the route. a) The second task is to identify and track a spherical object. The robot is equipped with a navigation system designed...
Classical Camshift algorithm (CCA) has been widely used in the fields of visual tracking, image smoothing and segmentation currently. In view of the fact that CCA is easily affected by the object which is similar to the tracking target in hue, an improved Camshift algorithm is presented in this paper. In this algorithm, the histogram equalization is applied simultaneously to R, G, B channels in order...
In this paper, a novel visual tracking algorithm that uses the target salient confidence (TSC) is proposed. Two contributions are summarized as follows. First, we put forward a novel target salient confidence (TSC) model which combines the static saliency map (SSM) based on the selective visual attention model, motion attention map (MAP) and the target prior confidence (TPC). Second, we propose to...
This paper presents the development of a first approach to a vision-based target detection. The ultimate objective of this work is to position an autonomous surface vehicle relative to a target. Experiments in a controlled indoor environment were conducted to test the developed system. The experimental results are analyzed and show that the tracking performances achieve errors in the order of a few...
This paper presents a novel multi-features fusion tracking algorithm based on local kernels learning. Histograms of multiple features are extracted based on sub image patches within the target region, and the features fusion weights are calculated respectively for each patch according to the discriminability of features. It means that the same feature employed in different sub image patches gets different...
This paper describes a novel RGB-D-based visual target tracking method for person-following robots. We enhance a single-object tracker, which combines RGB and depth information, by exploiting two different types of distracters. First set of distracters includes objects existing near-by the target, and the other set is for objects looking similar to the target. The proposed algorithm reduces tracking...
Particle filter shows great success in non-rigid object tracking. But tracking errors would be inevitable when the background changes greatly, or when the color distribution of background is similar to target's. To eliminate these errors, an improved method based on visual attention is proposed in this paper. In our algorithm, the extraction method based on visual attention is applied to extract salient...
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...
The problem of tracking a hand in video has gained a lot of attention due to its numerous applications in human computer interfaces. So far, the work has been limited to the use of standard speed videos, but the recent developments in imaging technology and computing hardware have made it attractive to exploit high-speed imaging for tracking the hand more accurately both in space and time. To produce...
Recent work in visual tracking has focussed on modelling target appearance, while using comparatively simple search methods to match those models to image data. Knowledge of the target's likely motion can both significantly reduce the search space and support more effective search strategies. We propose a new approach to target location which utilises sparse estimates of motion direction derived from...
This paper presents a robust object tracking method based on the methodologies of statistical texture analysis of 2D images based on the theory of monogenic signal analysis, jointed with the color histogram. This novel feature extraction method is embedded thereafter in the mean shift framework. Compared with methods of state-of-the-art mean shift trackers, this method proves to be more discriminant...
The purpose of this paper is to analyze passengers' moving direction through the video shot in the entrances and exits of the subway stations. The results of the analysis will be helpful to relevant departments to manage the traffic condition, making a decision in the face of emergency. First of all, this paper adopts Haar features and Adaboost algorithm to implement the detection of human's head...
An efficient algorithm is proposed that enhances the traditional mean-shift color histogram based object tracking approaches. We propose prominent local directional pattern variance (LDPT) that can extract directional texture responses and with color we develop a color-texture histogram representation for the target. Experiments show superior performance that allows target to go under complex environment...
In this paper, we present a soft biometrics based appearance model for multi-target tracking in a single camera. Track lets, the short-term tracking results, are generated by linking detections in consecutive frames based on conservative constraints. Our goal is to "re-stitching" the adjacent track lets that contain the same target so that robust long-term tracking results can be achieved...
Tracking a vary number of moving objects from an unsteady aerial platform has three major challenges. There are fast camera motion, changing scene and lighting, and targets entering and leaving the field of view at arbitrary position. In order to deal with these difficulties, a vision system that is capable of learning, detecting and tracking multiple objects of interest is developed. Our approach...
The 7th IARC (international aerial robot competition) focus on a long-term and real-time tracking of ground moving target for UAV (unmanned aerial vehicle). This paper proposes a tracking method using Camshift algorithm with color and edge features fusion, which will apply a Phash target detection algorithm basing on perceptual hash algorithm. Supposing targets are severely obscured or disappear from...
This paper presents a detection-based method for tracking an uncertain number of persons in complex scenarios with frequent occlusions. Frame-by-frame data association based particle filters are adopted to track targets in occlusion-free regions. When occlusion is detected, the associated trackers are deactivated and they are re-activated when the tracked persons are re-identified after occlusion...
This paper presents a novel image abstraction tech- nique, the deceived bilateral filter (DBF). The DBF combines border sharpening with simplification of homogeneous regions to achieve moderate de-blurring and colour emphasis. The aim of the DBF is to remove noise of colour and shape descriptors (histograms and contours) of the blobs corresponding to football players. The experiment implemented for...
This paper proposes a method to explore the user's navigation foci and visual tracks by estimating gaze points and mapping them to the objects of video content. The tracking method for multiple objects is derived from the adaptive weight based feature with probability densities. It is able to track the target objects efficiently even when the target objects are lost. It continuously applies sequence...
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