engleski

Sliding Window Object Detection without Spatial Clustering of Raw Detection Responses

Sliding window object detection has received remarkable attention in recent years due to great versatility and extraordinary detection performance. However, straightforward applications of the concept fail to meet criteria of real applications due to insufficient precision and inaccurate localization. Localization accuracy is especially important when the detection needs to be followed by recognition. In this paper, we present a detection approach which completely obviates the need for blind spatial clustering of nearby detection responses, which is known as a major factor of localization inaccuracy. The approach has been evaluated on traffic sign detection, where we consider the superclass of triangular warning signs. The obtained results confirm the viability of the approach and provide useful directions for future work.

object detection; sliding window; bootstrap training

DOI 978-3-902823-11-3