Effective and Efficient Detection of Moving Targets From a UAV's Camera

Accurate and fast detection of the moving targets from a moving camera are an important yet challenging problem, especially when the computational resources are limited. In this paper, we propose an effective, efficient, and robust method to accurately detect and segment multiple independently moving foreground targets from a video sequence taken by a monocular moving camera [e.g., onboard an unmanned aerial vehicle (UAV)]. Our proposed method advances the existing methods in a number of ways, where: 1) camera motion is estimated through tracking background keypoints using pyramidal Lucas-Kanade at every detection interval, for efficiency; 2) foreground segmentation is applied by integrating a local motion history function with spatio-temporal differencing over a sliding window for detecting multiple moving targets, while the perspective homography is used at image registration for effectiveness; and 3) the detection interval is adjusted dynamically based on a rule-of-thumb technique and considering camera setup parameters for robustness. The proposed method has been tested on a variety of scenarios using a UAV camera, as well as publically available data sets. Based on the reported results and through comparison with the existing methods, the accuracy of the proposed method in detecting multiple moving targets as well as its capability for real-time implementation has been successfully demonstrated. Our method is also robustly applicable to ground-level cameras for the ITS applications, as confirmed by the experimental results. More specifically, the proposed method shows promising performance compared with the literature in terms of quantitative metrics, while the run-time measures are significantly improved for real-time implementation.