Is a Detector Only Good for Detection?
|dc.identifier.citation||Yuan, Quan; Sclaroff, Stan. "Is a Detector Only Good for Detection?", Technical Report BUCS-TR-2009-023, Computer Science Department, Boston University, July 12, 2009. [Available from: http://hdl.handle.net/2144/1747]|
|dc.description.abstract||A common design of an object recognition system has two steps, a detection step followed by a foreground within-class classification step. For example, consider face detection by a boosted cascade of detectors followed by face ID recognition via one-vs-all (OVA) classifiers. Another example is human detection followed by pose recognition. Although the detection step can be quite fast, the foreground within-class classification process can be slow and becomes a bottleneck. In this work, we formulate a filter-and-refine scheme, where the binary outputs of the weak classifiers in a boosted detector are used to identify a small number of candidate foreground state hypotheses quickly via Hamming distance or weighted Hamming distance. The approach is evaluated in three applications: face recognition on the FRGC V2 data set, hand shape detection and parameter estimation on a hand data set and vehicle detection and view angle estimation on a multi-view vehicle data set. On all data sets, our approach has comparable accuracy and is at least five times faster than the brute force approach.||en_US|
|dc.description.sponsorship||National Science Foundation (ISS-0705749)||en_US|
|dc.publisher||Boston University Computer Science Department||en_US|
|dc.relation.ispartofseries||BUCS Technical Reports;BUCS-TR-2009-023|
|dc.title||Is a Detector Only Good for Detection?||en_US|
This item appears in the following Collection(s)