Browsing by Author "Ahmad, BMA"

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A novel object detection framework in compressed domain
(2004)
Ahmad, BMA
;
Ahmad, AMA
;
Lee, SY
;
資訊工程學系
;
Department of Computer Science
In this paper we propose a novel approach for robust motion vector based object detection in MPEG-2 video streams. By processing the extracted motion vector fields that are directly extracted from MPEG-2 video streams in the compressed domain, through post processing operation, in order to reduce the noise within the motion vector content, obtain more robust object information, and refine this information through our proposed system which composed of a Spatial filter Component, a Temporal filter Component and a Texture filter component. As a result, the object detection algorithm is more capable of accurately detecting objects with more efficient performance in terms of runtime. We compare the performance of our proposed system with other popular and commonly related work and techniques. Based on the experimental results performed over the MPEG7 testing dataset and measuring performance by using the standard recall and precision metrics, object detection using our proposed system is remarkably superior to the alternative techniques. In addition to these results, we describe a user system interface that we developed, where users can maintain the parameters interactively.
Enhancing the object detection by reference frame's AC components
(2004)
Ahmad, BMA
;
Ahmad, AMA
;
Lee, SY
;
資訊工程學系
;
Department of Computer Science
In this paper we propose a novel approach for Motion Vector (MV) based object detection in MPEG-2 video streams. Rather than processing the extracted MV fields that are directly extracted from MPEG-2 video streams in the compressed domain, we perform MV smoothing, perform MV noise reduction, obtain more robust object information, and refine this information through a texture filter. As a result, the object detection algorithm is more capable of accurately detecting objects. We compare the performance of our proposed system with the popular and commonly used spatial filter processing techniques in object detection domain: median filter, mean filter, Gaussian filter, and no filter Based on experimental results performed over the MPEG7 testing dataset and measuring performance using the standard recall and precision metrics, object detection using our texture filter is remarkably superior to the alternative filtering techniques. In addition to these results, we describe a user system interface that we developed, where users can maintain the filter parameters interactively.