Intelligent analysis oriented surveillance video coding
The fast growth of surveillance video big data presents great challenges to the video coding technology. Most existing video coding techniques target for visual quality optimization, while the ultimate utility of surveillance videos mainly lies in intelligent analyses, e.g., pedestrian detection and vehicle tracking. In view of this, we aim at proposing an efficient, standard-compatible and simultaneously analysis-friendly coding framework for intelligent surveillance videos. In particular, the foreground objects are first extracted from the video sequence by accurate background modeling. Subsequently, the foregrounds can be constructed as a sequence and compressed in higher quality while very few background pictures are required to signal in lower quality. At the decoder side, the foreground objects can be directly used for efficient analysis tasks and the surveillance videos can be also reconstructed by synthesizing background and foreground frames. The effectiveness and potential of the proposed framework have been demonstrated in the pedestrian detection application, where the coding bits can be greatly saved with the detection accuracy being well maintained.