Heterogeneous Computing for a Real-Time Pig Monitoring System

Video sensor data has been widely used in automatic surveillance applications. In this study, we present a method that automatically detects pigs in a pig room by using depth information obtained from a Kinect sensor. For a real-time implementation, we propose a means of reducing the execution time by applying parallel processing techniques. In general, most parallel processing techniques have been used to parallelize a specific task. In this study, we consider parallelization of an entire system that consists of several tasks. By applying a scheduling strategy to identify a computing device for each task and implementing it with OpenCL, we can reduce the total execution time efficiently. Experimental results reveal that the proposed method can automatically detect pigs using a CPU-GPU hybrid system in real time, regardless of the relative performance between the CPU and GPU.