Multi-GPU based event detection and localization using high definition videos

Video processing algorithms are widely used in applications related to computer vision such as motion tracking, human behavior understanding, event detection and localization. Nevertheless, the new video standards, in high definitions (HD: 1280×720, or Full HD: 1920×1080) cause that actual implementations, even running on modern hardware, can't respect the needs of real-time processing. To overcome this constraint, many applications have been developed, that exploit the high power of graphic processing units (GPUs). However, any is able to treat high definition videos efficiently. In this work, we propose an effective exploitation of single and multiple GPUs, in order to achieve real-time detection and localization of abnormal events, using HD and Full HD videos. The proposed approach detects portions of video that corresponds to sudden changes of motion variations of movements. It allows also to provide areas in video frames where motion behavior is surprising compared to the rest of motion in the same frame. Experimental results have been conducted using several videos showing efficient detection and localization of abnormal events in multi-user scenarios. The use of multiple GPUs enabled a real time treatment of high definition videos with a global speedup ranging from 5 to 35, by comparison with CPU implementations.