We study traffic sign detection on a challenging large-scale realworld dataset of panoramic images. The core processing is based on the Histogram of Oriented Gradients (HOG) algorithm which is extended by incorporating color information in the feature vector. The choice of the color space has a large influence on the performance, where we have found that the CIELab and YCbCr color spaces give the best results. The use of color significantly improves the detection performance. We compare the performance of a specific and HOG algorithm, and show that HOG outperforms the specific algorithm by up to tens of percents in most cases. In addition, we propose a new iterative SVM training paradigm to deal with the large variation in background appearance. This reduces memory consumption and increases utilization of background information.

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Radar is commonly used to detect and track ships in maritime surveillance. Unfortunately the systems are costly and do not provide any visual information about the object's type. To complement the ship identity information given by a radar system, we propose a supplementary system using active visual cameras that can robustly detect and track ships in harbours. By combining a high-quality, non real-time robust object detector with a feature point tracker with low computational complexity, it is possible to track ships in real time over long intervals and large distances. In addition to controlling pan and tilt, we dynamically control camera zoom to provide a high resolution image of the tracked object over a large range of distances. The tracking system is improved by a special motion estimation model for the feature points, which also incorporates zooming of the camera. The system is robust and sustains tracking even under challenging conditions, such as multiple viewpoints, a large variety of ships and various weather conditions. During experiments, various types of ships were successfully tracked for up to 18 minutes, and over a distance of almost 1.5 km in the port of Rotterdam. The proposed system is generic and can be utilized in various tracking applications, by training the detector for a different object class.