Rapid Detection of Many Object Instances

We describe an algorithm capable of detecting multiple object instances within a scene in the presence of changes in object viewpoint. Our approach consists of first calculating frequency vectors for discrete feature vector clusters (visual words) within a sliding window as a representation of the image patch. We then classify each patch using an AdaBoost classifier whose weak classifier simply applies a threshold to one visual word’s frequency within the patch. Compared to previous work, our algorithm is simpler yet performs remarkably well on scenes containing many object instances. The method requires relatively few training examples and consumes 2.2 seconds on commodity hardware to process an image of size 640×480. In a test on a challenging car detection problem using a relatively small training set, our implementation dramatically outperforms the detection performance of a standard AdaBoost cascade using Haar-like features.