Abstract 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-of-the-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).
When it comes to camera calibration in general, various approaches exist. The widely used method by @cite_11 uses a calibration checkerboard to obtain intrinsic and extrinsic camera parameters (relative to the checkerboard). @cite_8 use controlled panning or tilting with stereo matching to calibrate the camera. Correspondences of lines and points are used by @cite_4 . @cite_1 focus on automatic camera calibration for tennis videos from detected tennis court lines.
@cite_1 @cite_8 @cite_4 @cite_11
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This paper presents an original algorithm to automatically acquire accurate camera calibration from broadcast tennis video (BTV) as well as demonstrates two of its many applications. Accurate camera calibration from BTV is challenging because the frame-data of BTV is often heavily distorted and full of errors, resulting in wildly fluctuating camera parameters. To meet this challenge, we propose a frame grouping technique, which is based on the observation that many frames in BTV possess the same camera viewpoint. Leveraging on this fact, our algorithm groups frames according to the camera viewpoints. We then perform a group-wise data analysis to obtain a more stable estimate of the camera parameters. Recognizing the fact that some of these parameters do vary somewhat even if they have similar camera viewpoint, we further employ a Hough-like search to tune such parameters, minimizing the reprojection disparity. This two-tiered process gains stability in the estimates of the camera parameters, and yet ensures good match between the model and the reprojected camera view via the tuning step. To demonstrate the utility of such stable calibration, we apply the camera matrix acquired to two applications: (a) 3D virtual content insertion; and (b) tennis-ball detection and tracking. The experimental results show that our algorithm is able to acquire accurate camera matrix and the two applications have very good performances. Multiple cameras are increasingly prevalent on robotic and human-driven vehicles. These cameras come in a variety of wide-angle, fish-eye, and catadioptric models. Furthermore, wheel odometry is generally available on the vehicles on which the cameras are mounted. For robustness, vision applications tend to use wheel odometry as a strong prior for camera pose estimation, and in these cases, an accurate extrinsic calibration is required in addition to an accurate intrinsic calibration. To date, there is no known work on automatic intrinsic calibration of generic cameras, and more importantly, automatic extrinsic calibration of a rig with multiple generic cameras and odometry. We propose an easy-to-use automated pipeline that handles both intrinsic and extrinsic calibration; we do not assume that there are overlapping fields of view. At the begining, we run an intrinsic calibration for each generic camera. The intrinsic calibration is automatic and requires a chessboard. Subsequently, we run an extrinsic calibration which finds all camera-odometry transforms. The extrinsic calibration is unsupervised, uses natural features, and only requires the vehicle to be driven around for a short time. The intrinsic parameters are optimized in a final bundle adjustment step in the extrinsic calibration. In addition, the pipeline produces a globally-consistent sparse map of landmarks which can be used for visual localization. The pipeline is publicly available as a standalone C++ package. We present a dataset for evaluating the tracking accuracy of monocular visual odometry and SLAM methods. It contains 50 real-world sequences comprising more than 100 minutes of video, recorded across dozens of different environments -- ranging from narrow indoor corridors to wide outdoor scenes. All sequences contain mostly exploring camera motion, starting and ending at the same position. This allows to evaluate tracking accuracy via the accumulated drift from start to end, without requiring ground truth for the full sequence. In contrast to existing datasets, all sequences are photometrically calibrated. We provide exposure times for each frame as reported by the sensor, the camera response function, and dense lens attenuation factors. We also propose a novel, simple approach to non-parametric vignette calibration, which requires minimal set-up and is easy to reproduce. Finally, we thoroughly evaluate two existing methods (ORB-SLAM and DSO) on the dataset, including an analysis of the effect of image resolution, camera field of view, and the camera motion direction. In this paper we approach the problem of in- situ camera calibration using an auxiliary mobile color-depth camera. The calibration is based on image lines and the Direct Linear Transformation (DLT). Calibration includes intrinsic and extrinsic parameters, and radial distortion. Traditionally, camera calibration methods based on DLT combine 2D and 3D points. Using 2D image lines allows adding simple image processing methods for fine tuning the calibration data (lines). Experimental comparison of points and lines based calibration shows that the automatic fine tuning of the lines is beneficial for the calibration process in noisy conditions. Experiments on both synthetic and real setups show that despite the low computational complexity of the DLT, the proposed calibration methodology yields promising accurate results. I. I NTRODUCTION