Figure 1: We automatically determine 3 orthogonal vanishing points, construct vehicle bounding boxes (left), and automatically determine the camera scale by knowing the statistics of vehicle dimensions. This allows us to measure dimensions and speed (right) and analyze the traffic scene. This paper proposes a method for fully automatic calibration of traffic surveillance cameras. Our method allows for calibration of the camera -including scale -without any user input, only from several minutes of input surveillance video. The targeted applications include speed measurement, measurement of vehicle dimensions, vehicle classification, etc.The first step of our approach is camera calibration by determining three vanishing points defining the stream of vehicles (Fig. 2, [3]). The second step is construction of 3D bounding boxes of vehicles (Fig. 3) and their measurement up to scale. In the third step, we use the dimensions of the 3D bounding boxes for calibration of the scene scale ( The next step of our approach is construction of 3D bounding boxes of the observed vehicles (Fig. 3). We assume that the vehicle silhouettes can be extracted by background modeling and foreground detection and that the vehicles of interest are moving from/towards the first vanishing point. The 3D bounding box is constructed using tangent lines from vanishing points to the blob's boundary.Having the bounding box projection, it is directly possible to calculate the 3D bounding box dimensions (and position in the scene) up to precise scale. By fitting the statistics of known dimensions and the measured data from the traffic, we obtain the scale of the scene (Fig. 4).Camera orientation together with a know distance enables for measuring of vehicle speed/size or distances in the scene. We measured several

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This paper deals with detection and recognition of matrix codes, such as the QR codes, in high-resolution images of real-world scenes. The goal is to provide a detector capable of operation in real time even on high-resolution images (several megapixels). We present an efficient algorithm for detection of possible occurrences of the codes. This algorithm is characterized by a very low false negative rate and a reasonable false alarm rate. The results of our algorithm are to be followed by an accurate detection/recognition algorithm. We propose to use a recent matrix code detection and recognition algorithm based on Hough transform, because it can reuse some information computed by our new pre-detection algorithm and thus a further reduce of computational demands can be achieved. Since there are no publicly available annotated datasets for evaluation of this kind of algorithm, we collected a number of images and annotated them; these images will be made publicly available to allow for a proper comparison. Our algorithm was evaluated on this dataset and the results are reported in the paper.

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