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.

This paper aims at developing a real-time vessel detection and tracking system using surveillance cameras in harbours with the purpose to improve the current Vessel Tracking Systems (VTS) performance. To this end, we introduce a novel maritime dataset, containing 70,513 ships in 48,966 images, covering 10 camera viewpoints indicating real-life ship traffic situations. For detection, a Convolutional Neural Network (CNN) detector is trained, based on the Single Shot Detector (SSD) from literature. This detector is modified and enhanced to support the detection of extreme variations of ship sizes and aspect ratios. The modified SSD detector offers a high detection performance, which is based on explicitly exploiting the aspect-ratio characteristics of the dataset. The performance of the original SSD detector trained on generic object detection datasets (including ships) is significantly lower, showing the added value of a novel surveillance dataset for ships. Due to the robust performance of over 90% detection, the system is able to accurately detect all types of vessels. Hence, the system is considered a suitable complement to conventional radar detection, leading to a better operational picture for the harbour authorities. 1 APPS Project page: https://itea3.org/project/apps.html Zwemer, M., Wijnhoven, R. and With, P. Ship Detection in Harbour Surveillance based on Large-Scale Data and CNNs.

The CANDELA project aims at realizing a system for real-time image processing in traffic and surveillance applications. The system performs segmentation, labels the extracted blobs and tracks their movements in the scene. Performance evaluation of such a system is a major challenge since no standard methods exist and the criteria for evaluation are highly subjective. This paper proposes a performance evaluation approach for video content analysis (VCA) systems and identifies the involved research areas. For these areas we give an overview of the state-of-the-art in performance evaluation and introduce a classification into different semantic levels. The proposed evaluation approach compares the results of the VCA algorithm with a ground-truth (GT) counterpart, which contains the desired results. Both the VCA results and the ground truth comprise description files that are formatted in MPEG-7. The evaluation is required to provide an objective performance measure and a mean to choose between competitive methods. In addition, it enables algorithm developers to measure the progress of their work at the different levels in the design process. From these requirements and the state-of-the-art overview we conclude that standardization is highly desirable for which many research topics still need to be addressed.

Many proposed video content analysis algorithms for surveillance applications are very computationally intensive, which limits the integration in a total system, running on one processing unit (e.g. PC). To build flexible prototyping systems of low cost, a distributed system with scalable processing power is therefore required. This paper discusses requirements for surveillance systems, considering two example applications. From these requirements, specifications for a prototyping architecture are derived. An implementation of the proposed architecture is presented, enabling mapping of multiple software modules onto a number of processing units (PCs). The architecture enables fast prototyping of new algorithms for complex surveillance applications without considering resource constraints.