However, seeking an optimal combination of visual features which is generic yet adaptive to different benchmarks is a unsoved problem, and metric learning models easily get over-fitted due to the scarcity of training data in person re-identification.
The proposed framework consists of a dense feature extractor and detectors of three important classes.
We propose an effective structured learning based approach to the problem of person re-identification which outperforms the current state-of-the-art on most benchmark data sets evaluated.
Experimental results on both synthetic and real-world data sets demonstrate the effectiveness of our approach, and we show that it is possible to train state-of-the-art pedestrian detectors using the proposed structured ensemble learning method with spatially pooled features.
The combination of these factors leads to a pedestrian detector which outperforms all competitors on all of the standard benchmark datasets.
The use of high-dimensional features has become a normal practice in many computer vision applications.
We propose a novel ensemble learning method which achieves a maximal detection rate at a user-defined range of false positive rates by directly optimizing the partial AUC using structured learning.
In this work, we propose a scalable and simple stage-wise multi-class boosting method, which also directly maximizes the multi-class margin.