A general framework for object detection

TLDR

This paper proposes a general trainable framework for object detection in cluttered static images, including a motion‑based extension for video sequences. The method learns a compact wavelet‑based representation of object classes from statistical analysis of training samples, uses a subset of an overcomplete dictionary as input to a support vector machine, and is applied to face and people detection. The wavelet‑based representation yields low false‑positive rates and handles in‑class variability, and the system learns from examples without hand‑crafted models, indicating the architecture’s general applicability.

Abstract

This paper presents a general trainable framework for object detection in static images of cluttered scenes. The detection technique we develop is based on a wavelet representation of an object class derived from a statistical analysis of the class instances. By learning an object class in terms of a subset of an overcomplete dictionary of wavelet basis functions, we derive a compact representation of an object class which is used as an input to a support vector machine classifier. This representation overcomes both the problem of in-class variability and provides a low false detection rate in unconstrained environments. We demonstrate the capabilities of the technique in two domains whose inherent information content differs significantly. The first system is face detection and the second is the domain of people which, in contrast to faces, vary greatly in color, texture, and patterns. Unlike previous approaches, this system learns from examples and does not rely on any a priori (hand-crafted) models or motion-based segmentation. The paper also presents a motion-based extension to enhance the performance of the detection algorithm over video sequences. The results presented here suggest that this architecture may well be quite general.