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In this article, we present an algorithm to track objects in complex environments like, large variations in scale and orientation, background clutters, illumination changes, pose variation and occlusion. A multilayer perceptron based discriminative appearance model is constructed to distinguish the objects from their cluttered backgrounds. Moments of the binary image are used to estimate scale and...
In this paper, we propose an approach to learn hierarchical features for visual object tracking. First, we offline learn features robust to diverse motion patterns from auxiliary video sequences. The hierarchical features are learned via a two-layer convolutional neural network. Embedding the temporal slowness constraint in the stacked architecture makes the learned features robust to complicated...
We proposed a robust object tracking invariant object appearance variations and background clutter. Not only texture information is used, the depth information, which is important to classify the object from complicated background, is integrated as well. For the texture information, multiple instance learning with boosting algorithm is applied to select discriminant Haar-like features between the...
To deal with the drifting issue in visual tracking, we propose an Online Transfer Boosting (OTB) algorithm that transfers knowledge from three different source domains to the target domain to improve the performance of the online classifier used in tracking-by-detection. In particular, the OTB algorithm integrates three types of knowledge by: (1) transferring prior knowledge from the first frame using...
In this paper an adaptive Gaussian mixture model is introduced firstly to remove the shadow of regions of interest in the detection of moving human body from current video sequences. Then use a proposed method of obtaining ROI. From the view of the tracking effect, it can be concluded that this method of removal shadow of regions of interest can improve the precision rate of segment of moving people...
Many object tracking methods based on Adaptive Appearance Models (online learning methods) have been developed in recent years. One problem that can be found with these methods is how to learn variations in object appearance without errors in the image sequence. This paper introduces a novel method, in which a solution to remove learning errors by using an offline learning is proposed; in addition,...
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