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To overcome visual object tracking challenges, various feature-based object trackers use feature combination. Each feature component is developed to overcome certain tracking challenges, but the interaction between the components may cause tracking errors. We propose a tracking solution based on human vision principles to reduce combination errors by adaptively fusing each feature using its previous...
Persistent detection and tracking of moving vehicles in airborne imagery provide indispensable information for many traffic surveillance applications including traffic monitoring and management, navigation systems, activity recognition and event detection. This paper presents a collaborative Spatial Pyramid Context-aware detection and Tracking system (SPCT) for moving vehicles in dense urban aerial...
At present, the effective tracking of pedestrians is still a challenging task due to factors such as illumination change, pose variation, motion blur and occlusion. In this paper, we propose a simple and effective tracking algorithm which exploits the spatio-temporal context. Based on a existing Bayesian framework, we take full advantage of the relevance of the region of interest to its local context,...
Random forest has emerged as a powerful classification technique with promising results in various vision tasks including image classification, pose estimation and object detection. However, current techniques have shown little improvements in visual tracking as they mostly rely on piece wise orthogonal hyperplanes to create decision nodes and lack a robust incremental learning mechanism that is much...
A video segmentation method based on strong target constrained video saliency (STCVS) is proposed in this paper. In order to detect the salient region fast and effectively, the proposed STCVS is extracted based on the extension of image saliency by enforcing the salient region constrained with the location, scale and color model of the target. Besides, according to the results of STCVS, the super-pixel...
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...
Robust and accurate visual tracking is needed for many computer vision applications from video summarization to visual surveillance. Visual tracking remains to be a challenging task because of factors such as changing object appearance, illumination variations and shadows, partial and full occlusions, camera motion, distractors, and scale changes. Recently our group proposed a Likelihood of Features...
In order to improve the docking success rate in Automated Aerial Refueling (AAR), it is important to identify the receiver aircraft's receptacle for boom receptacle refueling (BRR). Meanshift tracking algorithm only considers the H component color statistics of the target area, lacking spatial information, could easily lead to inaccurate tracking. Besides, Meanshift tracking algorithm could easily...
Object tracking under complex circumstances remains to be a challenging problem because the appearance of an object can be drastically changed by illumination variations, pose variations and occlusion. This paper proposes an adaptive multiCfeature fusion strategy, in which the target appearance is modeled based on timed motion history image with HSV color histogram feature and edge orientation histogram...
Visual tracking is a vital task of computer vision, and becoming the basis of automated video surveillance. In this paper, we propose a novel tracking method which integrates two complementary trackers together, tracker A based on global appearance and tracker B based on a dynamic set of local features of the tracked object. Tracker A, an enhanced mean-shift tracker using the posterior probability...
Object trackers can be broadly classified into two types — feature based and color based. The feature based trackers are scale and illumination invariant whereas color trackers are better at handling occlusions and long term object detection. In this paper we propose a hybrid tracker that uses a feature based Circulant Structure tracker and a color based Mean Shift tracker running in parallel, that...
This paper proposes an approach for a tracking method robust to the intersection with objects with appearances similar to a target object. The proposed method targets image sequences taken by a moving camera and is based on the particle filter. Tracking methods using color information tend to track mistakenly a background region or an object with color similar to the target object. The method constructs...
This paper presents a new method for object tracking in a camera sensor with particle filters. The method enables multiple target and background models, arbitrarily spanning many features or imaging modalities, to be adaptively fused to provide optimal discriminating ability against changing backgrounds, which may present varying degrees of clutter and camouflage for different kinds of features at...
Robust scale calculation is a challenging problem in visual tracking. Most existing trackers fail to handle large scale variations in complex videos. To address this issue, we propose a robust and efficient scale calculation method in tracking-by-detection framework, which divides the target into four patches and computes the scale factor by finding the maximum response position of each patch via...
Visual tracking is one of the most active research areas in computer vision, with numerous application including augmented reality, surveillance, weapons-guiding technologies, and object identification. The key question for robust visual tracking is to extract the appearance model features of target appropriately and locate the object exactly. This paper presents a novel nonrigid target tracking algorithm...
The object tracking by using single feature is possible to generate errors and easy to lose the target if the illumination and object size scale are changed. We propose a particle-filter-object-tracking algorithm. The proposed algorithm is based on a covariance region descriptor (CRD). The CRD can fuse different features of a targeted object region while handling various complex backgrounds. Hence,...
Using color information in object tracking is a prudent choice, but the vast variety of choices and difficulties of obtaining a desirable stable result, unnerves many scholars. Color histograms, as a compact and robust representation is the center of attention while it suffers from lack of spatial information about colors. Besides, comparison and updating such histograms in a meaningful and efficient...
Micro Aerial Vehicles (MAV's) are becoming ubiquitous with its ever increasing applications in defense, space and environmental sectors. In real time scenario, MAV's are expected to perform autonomously and development of intelligent algorithms meant for pattern recognition and object tracking are most demanding. This work concentrates on the development of vision based navigation system for real...
Visual tracking of objects subjected to non-linear motion and appearance changes has shown to be a difficult task in computer vision. While research in visual object tracking has progressed significantly in terms of robust tracking of objects subjected to non-linear motion and appearance changes, these algorithms has shown limited capability for long term tracking of handheld objects during human-object...
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