Yoram Gdalyahu

Pedestrian detection for driving assistance systems: single-frame classification and system level performance

We describe the functional and architectural breakdown of a monocular pedestrian detection system. We describe in detail our approach for single-frame classification based on a novel scheme of breaking down the class variability by repeatedly training a set of relatively simple classifiers on clusters of the training set. Single-frame classification performance results and system level performance figures for daytime conditions are presented with a discussion about the remaining gap to meet a daytime normal weather condition production system.

Stereo-Assist: Top-down stereo for driver assistance systems

This paper presents a top-down approach to stereo for use in driver assistance systems. We introduce an asymmetric configuration where monocular object detection and range estimation is performed in the primary camera and then that image patch is aligned and matched in the secondary camera. The stereo distance measure from the matching assists in target verification and improved distance measurements. This approach, Stereo-Assist, shows significant advantages over the classical bottom-up stereo approach which relies on first computing a dense depth map and then using the depth map for object detection. The new approach can provide increased object detection range, reduced computational load, greater flexibility in camera configurations (we are no longer limited to side-by-side stereo configurations), greater robustness to obstructions in part of the image and mixed camera modalities FIR/VIS can be used. We show results with two novel configurations and illustrate how monocular object detection allows for simple online calibration of the stereo rig.