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In this paper, we propose a new framework in pedestrian detection using a two-step classification algorithm, which is a ??coarse to fine?? course. The framework consists of a full-body detection (FBD) step and a head-shoulder detection (HSD) step. The FBD step uses fusion of Haar-like and HOG features to get better performance, and the HSD step utilizes edgelet features for classification and detection...
We propose a method that can detect humans in a single image based on a novel cascaded structure. In our approach, both intensity-based rectangle features and gradient-based 1-D features are employed in the feature pool for weak-learner selection. The Real AdaBoost algorithm is used to select critical features from a combined feature set and learn the classifiers from the training images for each...
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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