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This paper proposes a new method to find face in real-time videos by combining detection and tracking. Basically, the method contains two complementary modules: detection by Viola Jones method and tracking by correlation filters. Detection in current frame is independent to previous frames, but the performance may downgrade under harsh conditions in terms of lighting, rotation, occlusion and others...
In this study, we model the disaster victim detection problem as a sub-problem of a larger casualty assessment problem, and propose a framework to solve it. The framework of algorithm independent components contains a victim detector, detection history component and a human robot interaction component that presents information obtained by the robot in a meaningful manner. The algorithm independence...
In this paper, we present a novel automatic marker detection method for X-ray images in the framework of machine learning, which is different from those approaches using traditional template matching or fitting algorithms based on prior knowledge. First we propose to use the covariance-based descriptors to effectively represent the marker features in X-ray images. Then we utilize the cascade of LogitBoost...
We propose a multi-person tracking framework using only one single camera in this paper. We utilize particle filter as the tracking framework and train a SVM classifier by reliable examples extracted from associated detections without occlusion. Based on the results of data association, we integrate the target's velocity into weights calculation to handle object occlusion assuming that fast-moving...
We propose a novel tracking algorithm based on insect vision inspired particle filter. In a cluttered moving background, flying insects demonstrate extraordinary capability in locating and detecting visual objects. Our tracker introduces an Elementary Motion Detector (EMD) which is deduced from the neuronal computational model of the way biological ommateum processing information, and integrates the...
In this paper, we propose a novel approach based on online learning for accurate and effective detection of abandoned objects. Most existing methods for abandoned objects detection only detect abandoned objects without considering of the logic owner of the abandoned object. These methods need an advanced trained human detector to discriminate abandoned objects from still persons frequently. However,...
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