HEVC compressed domain moving object detection and classfication
Liang Zhao, Debin Zhao, Xiaopeng Fan, Zhihai He · 2016
Compressed domain moving object segmentation and classification plays an important role in many real-time applications, such as video indexing and intelligent video surveillance. Compared with the previous international video coding standards, such as H.264/AVC, HEVC introduces a host of new coding features. Therefore, moving object segmentation and classification directly from HEVC compressed videos represents a new challenge. In this paper, we develop a method for segmenting and classifying moving objects, specifically, persons and vehicles, in the HEVC compression domain. We first train a classifier to determine if an image patch belongs to the foreground objects or background using HEVC syntax features. This will generate a bounding box which locates the object in the video frame. We then train a second classification model to classify the moving objects, either persons or vehicles, using bags of spatial-temporal HEVC syntax words. Our extensive experimental results demonstrate that the approach provides the remarkable performance and can classify moving person and vehicles accurately and robustly.