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To detect and localize objects is an indispensable step for many computer vision tasks. Most of the state-of-the-art methods of object detection and localization are category-dependent. These methods can achieve a significant performance. However, they are useless for detecting and localizing objects belonging to an unknown category when applying them to an unknown environment. In this paper, a method...
This paper presents a robust tracking algorithm for infrared objects in the image sequence, which is based on particle filer. Particle filter is a powerful tool for tracking especially in non-Gaussian condition, but the selection of samples is still a challenging problem. According to the frame-to-frame correlation, two basic assumptions are proposed. Borrowing the idea from Sequence Importance Sampling,...
Although monocular 2D tracking has been largely studied in the literature, it suffers from some inherent problems, mainly when handling persistent occlusions, that limit its performance in practical situations. Tracking methods combining observations from multiple cameras seem to solve these problems. However, most multi-camera systems require detailed information from each view, making it impossible...
In this paper we propose a particle filter based strategic approach to enhance the performance of visual tracking system with a new re-sampling algorithm. In any particle filter based application especially in visual tracking system, re-sampling is a vital process in the implementation of particle filtering. Usually it is a linear function of particle weight calculation to know the number of particle...
We propose to model a tracked object in a video sequence by locating a list of object features that are ranked according to their ability to differentiate against the image background. The Bayesian inference is utilised to derive the probabilistic location of the object in the current frame, with the prior being approximated from the pervious frame and the posterior achieved via the current pixel...
Tracking multiple interacting objects is an interesting and difficult task in computer vision. Two common problems in this field are a single object with multiple tracks and a single track with multiple objects. Most of the existing algorithms address the first problem but not the second one. In this paper, to solve the second problem we propose a new algorithm with a novel prediction model, which...
This paper proposes a new two-stage human detection method involving matching and verification. A Bayesian framework is developed to verify the matching score obtained from a weighted distance measure. Performance evaluation indicates that the proposed method is able to utilize the flexible matching scheme and produce superior true positive, true negative and low misclassification rates.
Automatic detection of landmarks, usually special places in the environment such as gateways, for topological mapping has proven to be a difficult task. We present the use of Bayesian surprise, introduced in computer vision, for landmark detection. Further, we provide a novel hierarchical, graphical model for the appearance of a place and use this model to perform surprise-based landmark detection...
This paper presents an algorithm designed to compute the perceived interest of objects in images. We measured likelihood functions via a psychophysical experiment in which subjects rated the perceived visual interest of 562 objects in 150 images. These results were then used to determine the likelihood of perceived interest given various factors such as location, contrast, color, and edge-strength...
Detection of scene transition is the first step on video segmentation, indexing and analysis. Although scene classification by human can be performed with visual or sonorous attributes at the same time, machine automatic classification usually relies on feature extraction of main visual characteristics. The use of color, shape, digital sound processing and voice signal altogether are investigated...
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