Designing robust activity detectors for fixed camera surveillance video requires knowledge of the 3-D scene. This paper presents an automatic camera calibration process that provides a mechanism to reason about the spatial proximity between objects at different times. It combines a CNN-based camera pose estimator with a vertical scale provided by pedestrian observations to establish the 4-D scene geometry. Unlike some previous methods, the people do not need to be tracked nor do the head and feet need to be explicitly detected. It is robust to individual height variations and camera parameter estimation errors.
@cite_14: For images taken in man-made scenes, vanishing points and focal length of camera play important roles in scene understanding. In this paper, we present a novel method to quickly, accurately and simultaneously estimate three orthogonal vanishing points (TOVPs) and focal length from single images. Our method is based on the following important observations: If we establish a polar coordinate system on the image plane whose origin is at the image center, angle coordinates of vanishing points can be robustly estimated by seeking peaks in a histogram. From the detected angle coordinates, altitudes of a triangle formed by TOVPs are determined. Novel constraints on both vanishing points and focal length could be obtained from the three altitudes. By using the constraints, radial coordinates of TOVPs and focal length can be estimated simultaneously. Our method decomposes a 2D Hough parameter space into two cascaded 1D Hough parameter spaces, which makes our method much faster and more robust than previous methods without losing accuracy. Enormous experiments on real images have been done to test feasibility and correctness of our method.
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@cite_3: The objective of this paper is to rectify any monocular image by computing a homography matrix that transforms it to a bird's eye (overhead) view. We make the following contributions: (i) we show that the homography matrix can be parameterised with only four parameters that specify the horizon line and the vertical vanishing point, or only two if the field of view or focal length is known; (ii) We introduce a novel representation for the geometry of a line or point (which can be at infinity) that is suitable for regression with a convolutional neural network (CNN); (iii) We introduce a large synthetic image dataset with ground truth for the orthogonal vanishing points, that can be used for training a CNN to predict these geometric entities; and finally (iv) We achieve state-of-the-art results on horizon detection, with 74.52 AUC on the Horizon Lines in the Wild dataset. Our method is fast and robust, and can be used to remove perspective distortion from videos in real time.
@cite_0: Known scene geometry and camera calibration parameters give important information to video content analysis systems. In this paper, we propose a novel method for camera pose estimation based on people observation in the input video captured by static camera. As opposed to previous techniques, our method can deal with false positive detections and inaccurate localization results. Specifically, the proposed method does not make any assumption about the utilized object detector and takes it as a parameter. Moreover, we do not require a huge labeled dataset of real data and train on the synthetic data only. We apply the proposed technique for camera pose estimation based on head observations. Our experiments show that the algorithm trained on the synthetic dataset generalizes to real data and is robust to false positive detections.
@cite_2: We propose a novel method for detecting horizontal vanishing points and the zenith vanishing point in man-made environments. The dominant trend in existing methods is to first find candidate vanishing points, then remove outliers by enforcing mutual orthogonality. Our method reverses this process: we propose a set of horizon line candidates and score each based on the vanishing points it contains. A key element of our approach is the use of global image context, extracted with a deep convolutional network, to constrain the set of candidates under consideration. Our method does not make a Manhattan-world assumption and can operate effectively on scenes with only a single horizontal vanishing point. We evaluate our approach on three benchmark datasets and achieve state-of-the-art performance on each. In addition, our approach is significantly faster than the previous best method.
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@cite_11: Person tracking across non-overlapping cameras and other types of video analytics benefit from spatial calibration information that allows an estimation of the distance between cameras and a relation between pixel coordinates and world coordinates within a camera. In a large environment with many cameras, or for frequent ad-hoc deployments of cameras, the cost of this calibration is high. This creates a barrier for the use of video analytics. Automating the calibration allows for a short configuration time, and the use of video analytics in a wider range of scenarios, including ad-hoc crisis situations and large scale surveillance systems. We show an autocalibration method entirely based on pedestrian detections in surveillance video in multiple non-overlapping cameras. In this paper, we show the two main components of automatic calibration. The first shows the intra-camera geometry estimation that leads to an estimate of the tilt angle, focal length and camera height, which is important for the conversion from pixels to meters and vice versa. The second component shows the inter-camera topology inference that leads to an estimate of the distance between cameras, which is important for spatio-temporal analysis of multi-camera tracking. This paper describes each of these methods and provides results on realistic video data. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).
Camera pose estimation for a static camera has been thoroughly researched @cite_11 @cite_13 @cite_4 @cite_14 @cite_6 @cite_0 @cite_9 @cite_5 @cite_2 . Abbas and Zisserman developed the Bird’s Eye View Network @cite_3 for estimation of focal length, camera pitch, and camera roll using image data without specific features. Other methods for featureless estimation of camera pose exist, but do not have code publicly available @cite_4 @cite_14 . There are also featureless convolutional neural networks that focus only on estimating the horizon line @cite_9 @cite_2 or focal length @cite_5 of the camera. Feature-based methods for camera pose estimation have also been created. Methods using pedestrian data as features to predict camera pose exist @cite_11 @cite_6 @cite_0 , but do not have code publicly available. The aforementioned paper on 3-D location estimation for vehicles @cite_13 also predicts camera pose from vehicle data, but requires the previously mentioned assumptions.