Is a detector only good for detection?

A common design of an object recognition system has two steps, a detection step followed by a foreground withinclass 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 ...

• Type: Conference
• Year: 2009
• Where: ICCV
• Authors: Quan Yuan and Stan Sclaroff
• Keywords: Computer Vision, ICCV 2009, Object Recognition