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Detecting vehicles in aerial images provides important information for traffic management and urban planning. Detecting the cars in the images is challenging due to the relatively small size of the target objects and the complex background in man-made areas. It is particularly challenging if the goal is near-real-time detection, i.e., within few seconds, on large images without any additional information,...
Tracking-by-detection is an attractive paradigm for intelligent visual surveillance applications where clutter, lighting variations, target overlap and occlusions hamper conventional background modeling. However, state-of-the-art vehicle and pedestrian detectors based on discriminative classification are too computationally expensive for real-time implementation on embedded smart cameras. This paper...
In this paper, we employ the recent on-line boosting framework to fuse heterogeneous features for object detection and tracking in a video surveillance application. Detection and tracking are treated as a classification problem by an ensemble of weak classifiers built on heterogeneous feature types and updated on-line. We extend the on-line boosting framework by proposing an algorithm that builds...
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