Adaptive Stick-Like Features for Human Detection Based on Multi-scale Feature Fusion Scheme

IEEE Computer Society
Publication Type:
Conference Proceeding
Proceedings. 2010 Digital Image Computing: Techniques and Applications (DICTA 2010), 2010, pp. 375 - 380
Issue Date:
Full metadata record
Files in This Item:
Filename Description SizeFormat
2010000746OK.pdf2.03 MBAdobe PDF
Human detection has been widely used in many applications. In the meantime, it is still a difficult problem with many open questions due to challenges caused by various factors such as clothing, posture and etc. By investigating several benchmark methods and frameworks in the literature, this paper proposes a novel method which successfully implements the Real AdaBoost training procedure on multi-scale images. Various object features are exposed on multiple levels. To further boost the overall performance, a fusion scheme is established using scores obtained at various levels which integrates decision results with different scales to make the final decision. Unlike other score-based fusion methods, this paper re-formulates the fusion process through a supervised learning. Therefore, our fusion approach can better distinguish subtle difference between human objects and non-human objects. Furthermore, in our approach, we are able to use simpler weak features for boosting and hence alleviate the training complexity existed in most of AdaBoost training approaches. Encouraging results are obtained on a well recognized benchmark database.
Please use this identifier to cite or link to this item: