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Person re-identification on image sets in which each image is taken from a different angle and lighting condition is a very challenging task. This task becomes even more difficult when images are low resolution and carrying image compression artifacts. The accuracy of the existing re- identification techniques are relatively low on the challenging evaluation grounds such as VIPeR and iLIDS image datasets...
Localizing license plate in an image enables vehicle detection and identification. Processing at high-definition (HD) image allows a better access of visual information but also enhances the necessity of multi-resolution analysis because license plates may appear in various sizes and shapes. A great computational burden is accompanied by processing the great number of candidate windows during multi-resolution...
The integration of object motion information to pixel-wise foreground segmentation is investigated for moving object detection applications. It is achieved by using a motion feedback mask as a basis to adapt parameter of background subtraction based algorithm. This binary mask indicates the possible location of foreground pixel via forwarding the previously detected object location and size. All pixels...
This paper proposes a no-reference video quality estimation model over burst loss wireless networks. The estimation model is an end-to-end, cross-layer framework which considers two parts as feature extraction and quality prediction. The first part considers content-aware parameters obtaining from rebuilt video sequences, transmission-aware parameters getting from network layer and encoding parameters...
When the object move fast or the frame rate of camera is low, the Mean shift tracker proposed may lost it. In order to overcome this disadvantage, an improved method is presented. Firstly we transform the image by the pyramid algorithm and computer Integral histogram of the low resolution image. Secondly, find the optical matching point as coarse object location through histogram match, then use Mean...
We propose two novel techniques for automatic summarisation of lengthy surveillance videos, based on selection of frames containing scenes most informative for rapid perusal and interpretation by humans. In contrast to other video summarisation methods, the proposed methods explicitly focus on foreground objects, via edge histogram descriptor and a localised foreground information quantity (entropy)...
Particle filtering framework is widely used on tracking applications. In surveillance systems, it often combines with color information to achieve visual object tracking. However, the resource usage of this framework, including memory bandwidth and operation cycles, is very intensive to make a low cost real time object tracking unattainable. In this paper, an efficient architecture of color based...
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