Exploiting Features – Locally Interleaved Sequential Alignment for Object Detection

Abstract

We exploit image features multiple times in order to make sequential decision process faster and better performing. In the decision process features providing knowledge about the object presence or absence in a given detection window are successively evaluated. We show that these features also provide information about object position within the evaluated window. The classification process is sequentially interleaved with estimating the correct position. The position estimate is used for steering the features yet to be evaluated. This locally interleaved sequential alignment (LISA) allows to run an object detector on sparser grid which speeds up the process. The position alignment is jointly learned with the detector. We achieve a better detection rate since the method allows for training the detector on perfectly aligned image samples. For estimation of the alignment we propose a learnable regressor that approximates a non-linear regression function and runs in negligible time.