An Epipolar Line from a Single Pixel

Computing the epipolar geometry from feature points between cameras with very different viewpoints is often error prone, as an object's appearance can vary greatly between images. For such cases, it has been shown that using motion extracted from video can achieve much better results than using a static image. This paper extends these earlier works based on the scene dynamics. In this paper we propose a new method to compute the epipolar geometry from a video stream, by exploiting the following observation: For a pixel p in Image A, all pixels corresponding to p in Image B are on the same epipolar line. Equivalently, the image of the line going through camera A's center and p is an epipolar line in B. Therefore, when cameras A and B are synchronized, the momentary images of two objects projecting to the same pixel, p, in camera A at times t1 and t2, lie on an epipolar line in camera B. Based on this observation we achieve fast and precise computation of epipolar lines. Calibrating cameras based on our method of finding epipolar lines is much faster and more robust than previous methods.Comment: WACV 201

What Do Color Changes Reveal About an Outdoor Scene?

In an extended image sequence of an outdoor scene, one observes changes in color induced by variations in the spectral composition of daylight. This paper proposes a model for these temporal color changes and explores its use for the analysis of outdoor scenes from time-lapse video data. We show that the time-varying changes in direct sunlight and ambient skylight can be recovered with this model, and that an image sequence can be decomposed into two corresponding components. The decomposition provides access to both radiometric and geometric information about a scene, and we demonstrate how this can be exploited for a variety of visual tasks, including color-constancy, background subtraction, shadow detection, scene reconstruction, and camera geo-location.Engineering and Applied Science

Cognitive visual tracking and camera control

Cognitive visual tracking is the process of observing and understanding the behaviour of a moving person. This paper presents an efficient solution to extract, in real-time, high-level information from an observed scene, and generate the most appropriate commands for a set of pan-tilt-zoom (PTZ) cameras in a surveillance scenario. Such a high-level feedback control loop, which is the main novelty of our work, will serve to reduce uncertainties in the observed scene and to maximize the amount of information extracted from it. It is implemented with a distributed camera system using SQL tables as virtual communication channels, and Situation Graph Trees for knowledge representation, inference and high-level camera control. A set of experiments in a surveillance scenario show the effectiveness of our approach and its potential for real applications of cognitive vision

Mesh-based 3D Textured Urban Mapping

In the era of autonomous driving, urban mapping represents a core step to let vehicles interact with the urban context. Successful mapping algorithms have been proposed in the last decade building the map leveraging on data from a single sensor. The focus of the system presented in this paper is twofold: the joint estimation of a 3D map from lidar data and images, based on a 3D mesh, and its texturing. Indeed, even if most surveying vehicles for mapping are endowed by cameras and lidar, existing mapping algorithms usually rely on either images or lidar data; moreover both image-based and lidar-based systems often represent the map as a point cloud, while a continuous textured mesh representation would be useful for visualization and navigation purposes. In the proposed framework, we join the accuracy of the 3D lidar data, and the dense information and appearance carried by the images, in estimating a visibility consistent map upon the lidar measurements, and refining it photometrically through the acquired images. We evaluate the proposed framework against the KITTI dataset and we show the performance improvement with respect to two state of the art urban mapping algorithms, and two widely used surface reconstruction algorithms in Computer Graphics.Comment: accepted at iros 201

Detecting, Tracking, And Recognizing Activities In Aerial Video

In this dissertation, we address the problem of detecting humans and vehicles, tracking them in crowded scenes, and finally determining their activities in aerial video. Even though this is a well explored problem in the field of computer vision, many challenges still remain when one is presented with realistic data. These challenges include large camera motion, strong scene parallax, fast object motion, large object density, strong shadows, and insufficiently large action datasets. Therefore, we propose a number of novel methods based on exploiting scene constraints from the imagery itself to aid in the detection and tracking of objects. We show, via experiments on several datasets, that superior performance is achieved with the use of proposed constraints. First, we tackle the problem of detecting moving, as well as stationary, objects in scenes that contain parallax and shadows. We do this on both regular aerial video, as well as the new and challenging domain of wide area surveillance. This problem poses several challenges: large camera motion, strong parallax, large number of moving objects, small number of pixels on target, single channel data, and low frame-rate of video. We propose a method for detecting moving and stationary objects that overcomes these challenges, and evaluate it on CLIF and VIVID datasets. In order to find moving objects, we use median background modelling which requires few frames to obtain a workable model, and is very robust when there is a large number of moving objects in the scene while the model is being constructed. We then iii remove false detections from parallax and registration errors using gradient information from the background image. Relying merely on motion to detect objects in aerial video may not be sufficient to provide complete information about the observed scene. First of all, objects that are permanently stationary may be of interest as well, for example to determine how long a particular vehicle has been parked at a certain location. Secondly, moving vehicles that are being tracked through the scene may sometimes stop and remain stationary at traffic lights and railroad crossings. These prolonged periods of non-motion make it very difficult for the tracker to maintain the identities of the vehicles. Therefore, there is a clear need for a method that can detect stationary pedestrians and vehicles in UAV imagery. This is a challenging problem due to small number of pixels on the target, which makes it difficult to distinguish objects from background clutter, and results in a much larger search space. We propose a method for constraining the search based on a number of geometric constraints obtained from the metadata. Specifically, we obtain the orientation of the ground plane normal, the orientation of the shadows cast by out of plane objects in the scene, and the relationship between object heights and the size of their corresponding shadows. We utilize the above information in a geometry-based shadow and ground plane normal blob detector, which provides an initial estimation for the locations of shadow casting out of plane (SCOOP) objects in the scene. These SCOOP candidate locations are then classified as either human or clutter using a combination of wavelet features, and a Support Vector Machine. Additionally, we combine regular SCOOP and inverted SCOOP candidates to obtain vehicle candidates. We show impressive results on sequences from VIVID and CLIF datasets, and provide comparative quantitative and qualitative analysis. We also show that we can extend the SCOOP detection method to automatically estimate the iv orientation of the shadow in the image without relying on metadata. This is useful in cases where metadata is either unavailable or erroneous. Simply detecting objects in every frame does not provide sufficient understanding of the nature of their existence in the scene. It may be necessary to know how the objects have travelled through the scene over time and which areas they have visited. Hence, there is a need to maintain the identities of the objects across different time instances. The task of object tracking can be very challenging in videos that have low frame rate, high density, and a very large number of objects, as is the case in the WAAS data. Therefore, we propose a novel method for tracking a large number of densely moving objects in an aerial video. In order to keep the complexity of the tracking problem manageable when dealing with a large number of objects, we divide the scene into grid cells, solve the tracking problem optimally within each cell using bipartite graph matching and then link the tracks across the cells. Besides tractability, grid cells also allow us to define a set of local scene constraints, such as road orientation and object context. We use these constraints as part of cost function to solve the tracking problem; This allows us to track fast-moving objects in low frame rate videos. In addition to moving through the scene, the humans that are present may be performing individual actions that should be detected and recognized by the system. A number of different approaches exist for action recognition in both aerial and ground level video. One of the requirements for the majority of these approaches is the existence of a sizeable dataset of examples of a particular action from which a model of the action can be constructed. Such a luxury is not always possible in aerial scenarios since it may be difficult to fly a large number of missions to observe a particular event multiple times. Therefore, we propose a method for v recognizing human actions in aerial video from as few examples as possible (a single example in the extreme case). We use the bag of words action representation and a 1vsAll multi-class classification framework. We assume that most of the classes have many examples, and construct Support Vector Machine models for each class. Then, we use Support Vector Machines that were trained for classes with many examples to improve the decision function of the Support Vector Machine that was trained using few examples, via late weighted fusion of decision values