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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We introduce a novel method for odometry estimation using convolutional neural networks from 3D LiDAR scans. The original sparse data are encoded into 2D matrices for the training of proposed networks and for the prediction. Our networks show significantly better precision in the estimation of translational motion parameters comparing with state of the art method LOAM, while achieving real-time performance. Together with IMU support, high quality odometry estimation and LiDAR data registration is realized. Moreover, we propose alternative CNNs trained for the prediction of rotational motion parameters while achieving results also comparable with state of the art. The proposed method can replace wheel encoders in odometry estimation or supplement missing GPS data, when the GNSS signal absents (e.g. during the indoor mapping). Our solution brings real-time performance and precision which are useful to provide online preview of the mapping results and verification of the map completeness in real time.

In this paper, we focus on fully automatic traffic surveillance camera calibration, which we use for speed measurement of passing vehicles. We improve over a recent state-ofthe-art camera calibration method for traffic surveillance based on two detected vanishing points. More importantly, we propose a novel automatic scene scale inference method. The method is based on matching bounding boxes of rendered 3D models of vehicles with detected bounding boxes in the image. The proposed method can be used from arbitrary viewpoints, since it has no constraints on camera placement. We evaluate our method on the recent comprehensive dataset for speed measurement BrnoCompSpeed. Experiments show that our automatic camera calibration method by detection of two vanishing points reduces error by 50 % (mean distance ratio error reduced from 0.18 to 0.09) compared to the previous state-of-the-art method. We also show that our scene scale inference method is more precise, outperforming both state-of-the-art automatic calibration method for speed measurement (error reduction by 86 % -7.98 km/h to 1.10 km/h) and manual calibration (error reduction by 19 % -1.35 km/h to 1.10 km/h). We also present qualitative results of the proposed automatic camera calibration method on video sequences obtained from real surveillance cameras in various places, and under different lighting conditions (night, dawn, day).

In this paper, we focus on traffic camera calibration and visual speed measurement from a single monocular camera, which is an important task of visual traffic surveillance. Existing methods addressing this problem are hard to compare due to a lack of a common dataset with reliable ground truth. Therefore, it is not clear how the methods compare in various aspects and what are the factors affecting their performance. We captured a new dataset of 18 full-HD videos, each around one hour long, captured at 6 different locations. Vehicles in the videos (20 865 instances in total) are annotated with precise speed measurements from optical gates using LIDAR and verified with several reference GPS tracks. We made the dataset available for download and it contains the videos and metadata (calibration, lengths of features in image, annotations, etc.) for future comparison and evaluation. Camera calibration is the most crucial part of the speed measurement; therefore, we provide a brief overview of the methods and analyze a recently published method for fully automatic camera calibration and vehicle speed measurement and report the results on this dataset in detail.

In this paper, we focus on fine-grained recognition of vehicles mainly in traffic surveillance applications. We propose an approach that is orthogonal to recent advancements in finegrained recognition (automatic part discovery, bilinear pooling). Also, in contrast to other methods focused on fine-grained recognition of vehicles, we do not limit ourselves to a frontal/rear viewpoint, but allow the vehicles to be seen from any viewpoint. Our approach is based on 3D bounding boxes built around the vehicles. The bounding box can be automatically constructed from traffic surveillance data. For scenarios where it is not possible to use precise construction, we propose a method for an estimation of the 3D bounding box. The 3D bounding box is used to normalize the image viewpoint by "unpacking" the image into a plane. We also propose to randomly alter the color of the image and add a rectangle with random noise to a random position in the image during the training of Convolutional Neural Networks. We have collected a large fine-grained vehicle dataset BoxCars116k, with 116k images of vehicles from various viewpoints taken by numerous surveillance cameras. We performed a number of experiments which show that our proposed method significantly improves CNN classification accuracy (the accuracy is increased by up to 12 percentage points and the error is reduced by up to 50 % compared to CNNs without the proposed modifications). We also show that our method outperforms stateof-the-art methods for fine-grained recognition.

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