Algorithm and architecture design of human-machine interaction in foreground object detection with dynamic scene

In the field of intelligent visual surveillance, the topic of tolerating background motions while detecting foreground motions in dynamic scene is widely explored by the recent foreground detection literatures. Applying the sophisticated background modeling method is a common solution for such dynamic background problem. However, the sophisticated background modeling method is computation intensive and involves huge memory bandwidth on data access. Realizing such approach on a multicamera surveillance system for real-time application can dramatically increase the hardware cost. This paper presents a hardware-oriented foreground detection that is based on human-machine interaction in object level (HMIiOL) scheme. The HMIiOL can vary the conditions for a moving object been regarded as a foreground object. The conditions are depending on background environment and are derived from the information from human-machine interaction. By the HMIiOL scheme, adopting a simple background modeling method can achieve well foreground detection with significant background motions. A processor based on system-on-chip design is presented for the HMIiOL-based foreground detection. The presented processor consists of accelerators to increase throughput of the computationally intensive tasks in the algorithm, and a reduced instruction set computing unit to handle the interaction task and the noncomputation-intensive tasks. Pipelining and parallelism techniques are used to increase the throughput. The detecting capability of the processor reaches HD720 at 30 Hz. The maximum throughput can be up to 32.707 Mpixels/s. Performance evaluation and comparison with existed foreground detection hardware show the improvement of our design.