Human attribute analysis using a top-view camera based on multi-stage classification

This paper proposes pedestrians' attribute analysis such as gender and whether they have bags with them based on multi-layer classification. One of the technically challenging issues is we use only top-view camera images to protect the privacy of the pedestrians. The shape features over the frames are extracted by bag-of-features (BoF) using histogram of oriented gradients (HoG) vectors with the optimized parameters. Then, multiple classifiers using support vector machine (SVM) were generated by changing the parameters for the feature generation. A set of classification results using the multiple classifiers is fed to the second stage classifier to obtain the final results. The experimental results using 60-minute video captured at Haneda Airport, Japan, show that the accuracies for the gender classification and the with/without baggage classification were 95.8% and 97.2%, respectively with low false positive/negative rates, which is a significant improvement from our previous work which yielded 68.5% and 78.8% of accuracy, respectively.