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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...
This paper describes algorithms for Autonomous Surface Vehicle(ASV) obstacle avoidance and target search task. This work is primarily designed for task mission of 2016 Maritime RobotX competition. In this task, ASV must avoid obstacle buoys, while it is searching for totem-shaped buoy. To deal with such problem, algorithms for both perception and motion planning stage was designed. In perception stage,...
In this paper, a human recognition method based on soft biometrics is proposed for the human following robot. Two soft biometric traits (clothes color and body size) are calculated as features of the human. First, the human region detected by the Kinect is segmented to obtain the torso and leg parts of the body. Then the weighted HSV histograms of the body parts are calculated to describe the clothes...
Target tracking using color based appearance models is very popular in visual tracking. However, trackers based only on color are fragile and often drift to the background when it has similar appearances. In this paper, we propose an efficient way to use distinctive target colors to track the target and eliminate the drift problem. Colors are sampled from the target and its immediate surrounding region...
Human tracking in crowded scenes is a challenging problem because of frequent occlusion and presence of the tracking in similar regions. In this paper, we propose an online human tracking method which can handle occlusion and targets with similar regions. Our method compares the target region with a surrounding region and targets with similar regions at current frame. In addition, we also compare...
In this paper, we address the problem of online RGB-D tracking where the target object undergoes significant appearance changes. To sufficiently exploit the color and depth cues, we propose a novel RGB-D tracking framework (DLS) that simultaneously builds the target 2D appearance model and 3D distribution model. The framework decomposes the tracking task into detection, learning and segmentation....
Robust visual tracking is a challenging computer vision problem, with many real-world applications. Most existing approaches employ hand-crafted appearance features, such as HOG or Color Names. Recently, deep RGB features extracted from convolutional neural networks have been successfully applied for tracking. Despite their success, these features only capture appearance information. On the other...
In this paper, we propose an effective visual tracking method based on candidates selected by deep features and CNSGM. Higher level deep features contain semantic information, which enables the tracker focus more on the target than on the background. A certain convolutional layer contains many neurons. Different neurons response different things in the image. We pick the neuron has larger response...
This paper proposes a novel multi-target video tracking (MTVT) method based on improved data association and mixed Kalman/$H_{\infty }$ filtering. First, multiple features of video targets, such as local texture, spatial color distribution, and edge-oriented gradients, are extracted to form the fused-feature matching matrix. Second, an efficient and accurate video targets association method integrating...
Object tracking over image sequences plays an remarkably crucial role in several computer vision applications, interalia, automated video surveillance, unmanned aerial vehicles and 3D reconstruction. In this paper, a novel, accurate, robust and recoverable real-time feature-based tracking framework is presented. The appearance modelling consists of a local and global layer. We propose a recommended...
This paper propose a study of real-time human gait tracking system based on a cascaded particle filter implementation using Microsoft Kinect sensor. Our tracking system is combination of two different levels which processing both color and depth information. In the first level, we utilize color histogram to implement a coarse 2D region tracking. For the second level, we implemented two different depth...
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...
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...
In this paper, we present a visual mechanism and a visual saliency model which is suitable for the visual tracking algorithm. In order to extract robust feature of motion area, instead of using saliency map to detect moving targets, our algorithm adopts a bottom-up attention model based on the human visual information processing mechanism. The method is robust to illumination and viewpoint changes...
The human traffic in each bus station is estimated in real-time by means of an embedded ARM(Acorn RISC Machine) platform and the API(Application Programming Interface) in OpenCV(Open Source Computer Vision). For high-density human traffic situations, this paper proposed a method in which the Hough Circle Transform, centroid coordinates and color are used as the features for detecting, tracking and...
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...
Auto tracking and target locking are an automated weapon system. The system works by tracking and locking target automatically against targets selected. This system can replace the human role in a defence point. The existing technologies utilize radar and opto-electrical technology. These technologies are vulnerable against jamming and have a high degree of difficulty and the cost of procurement that...
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...
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