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Object deformation and occlusion are ubiquitous problems for visual tracking. Though many efforts have been made to handle object deformation and occlusion, most existing tracking algorithms fail in case of large deformation and severe occlusion. In this paper, we propose a graph learning-based tracking framework to handle both challenges. For each consecutive frame pair, we construct a weighted graph,...
Moving object tracking with discriminative model is very popular in recent years, which focuses on online selecting highly informative features to maximize the separability between object and background. An adapted particle filter tracker with online learning and inheriting discriminative model is proposed in this paper. Top-ranked discriminative features are selected into appearance model by Online...
AbstractłIn order to enhance the robustness of kernel correlation filters(KCF) in complex background environment, this paper proposes a mean shift method with adaptive local object tracking algorithm. KCF algorithm has speed advantage by using the single template, we introduce the confidence map in the process of the tracking to determine the result of the current frame. If the result of confidence...
Visual object tracking is an active research field in the area of computer vision. The tracking process usually includes the construction of an object appearance model and the object localization. This paper investigates the use of Particle Swarm Optimization (PSO) as the object localization method based on the Bayesian tracking framework. The widely adopted particle filter tracking technique, however,...
Tracking-by-detection methods treat the target location as a classification problem in which the approach SVM + HOG shows a good performance. However, training a good SVM classifier is cost expensive. In this paper, we replace SVM by linear discriminant analysis (LDA) for classification where the mean and covariance of negative examples are evaluated only once. Not only the training is much cheaper,...
The change of appearance of the target object is one of important issue in visual tracking. It is because some factors such as camera motion, illumination change, motion change, occlusion, and size change are influenced to the object target during tracking. Recently, discriminative correlation filters (DCF) gave good results to handle these problems. Unfortunately, the DCF only works in the single-resolution...
In recent trends the movable object detection is locating its position & location with reference of higher weighted particles. The color based target detection & tracking is the main role for developing the application like video streaming, research area like color template matching processing & open source visual surveillance area. A Bayesian filtering method & video analysis modeling...
Correlation filter based trackers are well studied for object tracking and shown great interest to the research community in recent years. The vast majority of the works make utilization of either color feature channels or Histogram of Gradient feature channels for object tracking in visual spectrum. However, the strength of feature channels varies from RGB videos to thermal infrared videos. Subsequently,...
In recent years, vision based technologies have gained immense attention across academia-industries to enable optimal surveillance solution for event monitoring, analysis and control. However, the complexities of real time environment and expected functional characteristics often put question over existing approaches and their efficacy. In this paper, a number of the existing approaches for vision...
The color and gradient based sequential state estimation method has proved its applicability in many video based tracking applications. This paper proposes a multi-modal approach applicable to trajectory formation of multiple moving objects with complex random motion structure. The Bayesian framework for tracking is formulated in this paper that incorporate spatio temporal information in selecting...
Visual tracking is a challenging issue in surveillance, human–computer Interaction, and intelligent robotics, among others. Managing appearance changes of the target object, illumination changes, rotations, non-rigid deformations, partial or full occlusions, background clutter, fast motion, and so forth is generally difficult. Among the numerous existing trackers, the correlation-filter-based tracker...
Object tracking in real time is one of the most important topics in the field of computer Vision. The work undertaken in this dissertation is mainly focused on development of a reliable and robust real time tracking system that can track the object of interest in the video acquired from a stationary or moving camera. The proposed algorithm is a real time algorithm that operates in 25 frames per second...
In this paper, we present a method that combines a sparse appearance model into the Bayesian inference framework for tracking pedestrians in video sequences captured by a fixed camera. We formulate sparse appearance model as a linear combination of a set of 4D smoothed colour histograms for each pedestrian. These colour histograms are computed for all detection windows with different confidence values...
An algorithm based on particle filters is employed to track moving objects in video streams from fixed and non-fixed cameras. Particle weighting is based on color histograms computed in the iHLS color space. Particle computations are parallelized with CUDA framework. The algorithm was tested on various GPU devices: a desktop GPU card, a mobile chipset and two embedded GPU platforms. The processing...
Humans have the capability to quickly prioritize external visual stimuli and localize their most interest in a scene. Inspired by this mechanism, we propose a robust object tracking algorithm based on visual attention. We fuse motion feature and color feature to estimate the target state under the guidance of saliency map. Principal Component Analysis method is used to compute saliency feature based...
In this paper, we present an integrated approach of multiple algorithms for visual localization and object tracking. First, a spatial point monocular vision range model is established. Combined with vision technology, we can deduce a precise position of the target in the world frame, and the region of recognized object is regarded as the real tracking region. Second, The Camshift/Kalman/Particle algorithm...
In order to accurately track sea cucumber on the assembly line to realize automatic grabbing using mechanical arm, an object tracking method based on Mean-shift algorithm was proposed. Firstly, the contours of the objects was extracted from the original image to select tracking target, and then the local image was cropped at the same position and local area in the second frame, and mean-shift algorithm...
Correlation filter-based trackers achieve very good performance in visual tracking, but the traditional correlation tracking methods failed in mining the color information of the image sequence. To solve this problem, we propose a novel and robust scale adaptive tracker combined with color attributes in correlation filter framework, which extracts not only gray but also color information as the feature...
This paper presents a novel object tracking algorithm. Object appearance and spatial information is learned from a single template using a non-linear subspace projection. A probabilistic search strategy, based on particle filter, is employed to find object region in each frame of the video sequence that best models the target object in the subspace representation. Particle filter estimates the posterior...
This paper addresses an efficient mean shift object tracking algorithm, by employing the joint color-texture histogram for object representation, and the mean shift algorithm for object tracking. The textural information of the object is extracted by using the four directional code called the local tetra pattern. The Ohta color model has been used for extracting color information. The local tetra...
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