HYBRID VIDEO OBJECT TRACKING IN H.265/HEVC VIDEO STREAMS

In this paper we propose a hybrid tracking method which detects moving objects in videos compressed according to H.265/HEVC standard. Our framework largely depends on motion vectors (MV) and block types obtained by partially decoding the video bitstream and occasionally uses pixel domain information to distinguish between two objects. The compressed domain method is based on a Markov Random Field (MRF) model that captures spatial and temporal coherence of the moving object and is updated on a frame-to-frame basis. The hybrid nature of our approach stems from the usage of a pixel domain method that extracts the color information from the fully-decoded I frames and is updated only after completion of each Group-of-Pictures (GOP). We test the tracking accuracy of our method using standard video sequences and show that our hybrid framework provides better tracking accuracy than a state-of-the-art MRF model.