Real-Time Moving Object Segmentation and Classification From HEVC Compressed Surveillance Video

Abstract

Moving object segmentation and classification from compressed video plays an important role in intelligent video surveillance. Compared with H.264/AVC, High Efficiency Video Coding (HEVC) introduces a host of new coding features that can be further exploited for moving object segmentation and classification. In this paper, we present a real-time approach to segment and classify moving objects using unique features directly extracted from the HEVC compressed domain for video surveillance. In the proposed method, first, motion vector (MV) interpolation for intra-coded prediction unit (PU) and MV outlier removal are employed for preprocessing. Second, blocks with nonzero MVs are clustered into the connected foreground regions using the four-connectivity component labeling algorithm. Third, 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 PU size. Finally, a person–vehicle classification model using bag of spatial–temporal HEVC syntax words is trained to classify the moving objects, either persons or vehicles. The experimental results demonstrate that the proposed method provides solid performance and can classify moving persons and vehicles accurately.