Finding Stuff on the Street

General object detection still remains a big challenge for vision researchers. In this paper, we are particularly interested in the subject of object detection in the context of street scene. Our image database consists of video frames taken from urban street which tends to be crowded and presents a lot of artificial objects. Traditional street scene understanding methods often involve 3D reconstruction of the street scene before object detection. We argue that through carefully-chosen features and utilizing category-dependent detectors, we can still achieve good detection performance thus gain good understanding of street scene by merely low quality 2D images. In our detection framework,we use hybrid detectors for different object categories. For example, basic SVM classifier is adopted to detect rigid objects like traffic lights, traffic sign, lamp and fire hydrant; texture objects like trees are detected via a discriminative texture classifier; while for semi-rigid and multiple view objects like cars, votingbased detector is applied. We further prune false positives by utilizing appearance cues. Experiment result shows our method is able to recognize meaningful objects on street and gives attention to drivers or directions to auto-driven vehicles.