Abstract
Traffic scene perception (TSP) aims to extract accurate real-time on-road environment information, which involves three phases detection of objects of interest, recognition of detected objects, and tracking of objects in motion. In this project, we focus on three important classes of objects: traffic signs, cars, and cyclists. We propose to detect all the three important objects in a single learning-based detection framework. To enhance the feature robustness to noises and image deformations, we introduce spatially pooled features as a part of aggregated channel features. In order to further improve the generalization performance, we propose an object sub categorization method as a means of capturing the intra class variation of objects. We experimentally demonstrate the effectiveness and efficiency of the proposed framework in three detection applications: traffic sign detection, car detection, and cyclist detection.
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