Automatic Segmentation Of Moving Object From HEVC Compressed Surveillance Video-Survey

Abstract

Moving object segmentation and classification from compressed video plays an important
role for intelligent video surveillance. Compared with H.264/AVC, HEVC introduces a host
of new coding features which can be further exploited for moving object segmentation and
classification. In this paper, we present a real-time approach to segment and classify moving
object using unique features directly extracted from the HEVC compressed domain for video
surveillance. In the proposed method, firstly, motion vector interpolation for intra-coded
prediction unit and MV outlier removal are employed for preprocessing. Secondly, blocks
with non-zero motion vectors are clustered into the connected foreground regions using the
four connectivity component labeling algorithm. Thirdly, object region tracking based on
temporal consistency is applied to the connected foreground regions to remove the noise
regions. The boundary of moving object region is further refined by the coding unit size and
prediction unit size. Finally, a person-vehicle classification model using bag of spatialtemporal HEVC syntax words is trained to classify the moving objects, either persons or
vehicles. The experimental results demonstrate that the proposed method provides the solid
performance and can classify moving persons and vehicles accurately.