|dc.description.abstract||Visual tracking has been a popular application in computer vision, for example, public area surveillance, and robot vision, etc. This paper presents a robust and efficient algorithm for detecting and tracking moving objects, so that we can achieve an embedded system for real-time detection and tracking of moving objects.
For detection, we utilize a progressive and adaptive background generation. Even if the unstable noise, for example, light changed and fluttering leaves, we still extract the foreground objects exactly. Then particle swarm optimization (PSO) is used for tracking as a search strategy. First, build the target model, and through the PSO, we can track moving objects in the nonlinear system. Experiment shows that the proposed method can track the single person, multiple people even when occluded, and is more efficient and accurate than the traditional methods.
Eventually, we transplant the algorithm into the embedded system, and the CPU is ARM Cortex-M3. The COMS sensor is placed on a pan/tilt platform in order to track the moving object. Limited on the CPU and memory, the experiments and analysis still show the efficiency.