Target Detection from MPEG Video Based on Low-Rank Filtering in the Compressed Domain

There are advantages of using the motion vector obtained from the MPEG video coding to perform target of interest identification in the field. In practice, however, environment noise, time-varying, and uncertainty factors affect their performance reliably and accurately detecting targets of interest. In this paper, we proposed a novel low rank filtering based on L-1 norm in order to straighten up single rogue or outliers that might show up fairly often. Finally, a simple average smoothing filter was applied to reduce vector quantization noise. By using the low rank filtering based on L-1 norm, the dominant motion vectors from the MPEG video coding can be extracted appropriately with respect to target operational responses and can be used for robust identification of moving target. The performance of the proposed approach was evaluated based on a set of experimental camera motion. The motions, including pan, tilt, and zoom, was computed from the motion vectors, and the residual vectors which are not described by the camera motion are regarded as generated by moving blobs. Events, as a result, can be detected from these moving blobs. It is demonstrated that the approach yields very promising results where motion vectors obtained from the MPEG video coding can be used efficiently to detect and identify moving target in the field.