Object Association Across Multiple Moving Cameras In Planar Scenes

In this dissertation, we address the problem of object detection and object association across multiple cameras over large areas that are well modeled by planes. We present a unifying probabilistic framework that captures the underlying geometry of planar scenes, and present algorithms to estimate geometric relationships between different cameras, which are subsequently used for co-operative association of objects. We first present a local1 object detection scheme that has three fundamental innovations over existing approaches. First, the model of the intensities of image pixels as independent random variables is challenged and it is asserted that useful correlation exists in intensities of spatially proximal pixels. This correlation is exploited to sustain high levels of detection accuracy in the presence of dynamic scene behavior, nominal misalignments and motion due to parallax. By using a non-parametric density estimation method over a joint domain-range representation of image pixels, complex dependencies between the domain (location) and range (color) are directly modeled. We present a model of the background as a single probability density. Second, temporal persistence is introduced as a detection criterion. Unlike previous approaches to object detection that detect objects by building adaptive models of the background, the foreground is modeled to augment the detection of objects (without explicit tracking), since objects detected in the preceding frame contain substantial evidence for detection in the current frame. Finally, the background and foreground models are used competitively in a MAP-MRF decision framework, stressing spatial context as a condition of detecting interesting objects and the posterior function is maximized efficiently by finding the minimum cut of a capacitated graph. Experimental validation of the method is performed and presented on a diverse set of data. We then address the problem of associating objects across multiple cameras in planar scenes. Since cameras may be moving, there is a possibility of both spatial and temporal non-overlap in the fields of view of the camera. We first address the case where spatial and temporal overlap can be assumed. Since the cameras are moving and often widely separated, direct appearance-based or proximity-based constraints cannot be used. Instead, we exploit geometric constraints on the relationship between the motion of each object across cameras, to test multiple correspondence hypotheses, without assuming any prior calibration information. Here, there are three contributions. First, we present a statistically and geometrically meaningful means of evaluating a hypothesized correspondence between multiple objects in multiple cameras. Second, since multiple cameras exist, ensuring coherency in association, i.e. transitive closure is maintained between more than two cameras, is an essential requirement. To ensure such coherency we pose the problem of object associating across cameras as a k-dimensional matching and use an approximation to find the association. We show that, under appropriate conditions, re-entering objects can also be re-associated to their original labels. Third, we show that as a result of associating objects across the cameras, a concurrent visualization of multiple aerial video streams is possible. Results are shown on a number of real and controlled scenarios with multiple objects observed by multiple cameras, validating our qualitative models. Finally, we present a unifying framework for object association across multiple cameras and for estimating inter-camera homographies between (spatially and temporally) overlapping and non-overlapping cameras, whether they are moving or non-moving. By making use of explicit polynomial models for the kinematics of objects, we present algorithms to estimate inter-frame homographies. Under an appropriate measurement noise model, an EM algorithm is applied for the maximum likelihood estimation of the inter-camera homographies and kinematic parameters. Rather than fit curves locally (in each camera) and match them across views, we present an approach that simultaneously refines the estimates of inter-camera homographies and curve coefficients globally. We demonstrate the efficacy of the approach on a number of real sequences taken from aerial cameras, and report quantitative performance during simulations

Enabling Cross-Camera Collaboration for Video Analytics on Distributed Smart Cameras

Overlapping cameras offer exciting opportunities to view a scene from different angles, allowing for more advanced, comprehensive and robust analysis. However, existing visual analytics systems for multi-camera streams are mostly limited to (i) per-camera processing and aggregation and (ii) workload-agnostic centralized processing architectures. In this paper, we present Argus, a distributed video analytics system with cross-camera collaboration on smart cameras. We identify multi-camera, multi-target tracking as the primary task of multi-camera video analytics and develop a novel technique that avoids redundant, processing-heavy identification tasks by leveraging object-wise spatio-temporal association in the overlapping fields of view across multiple cameras. We further develop a set of techniques to perform these operations across distributed cameras without cloud support at low latency by (i) dynamically ordering the camera and object inspection sequence and (ii) flexibly distributing the workload across smart cameras, taking into account network transmission and heterogeneous computational capacities. Evaluation of three real-world overlapping camera datasets with two Nvidia Jetson devices shows that Argus reduces the number of object identifications and end-to-end latency by up to 7.13x and 2.19x (4.86x and 1.60x compared to the state-of-the-art), while achieving comparable tracking quality.Comment: 18 pages, under revie

Flight Dynamics-based Recovery of a UAV Trajectory using Ground Cameras

We propose a new method to estimate the 6-dof trajectory of a flying object such as a quadrotor UAV within a 3D airspace monitored using multiple fixed ground cameras. It is based on a new structure from motion formulation for the 3D reconstruction of a single moving point with known motion dynamics. Our main contribution is a new bundle adjustment procedure which in addition to optimizing the camera poses, regularizes the point trajectory using a prior based on motion dynamics (or specifically flight dynamics). Furthermore, we can infer the underlying control input sent to the UAV's autopilot that determined its flight trajectory. Our method requires neither perfect single-view tracking nor appearance matching across views. For robustness, we allow the tracker to generate multiple detections per frame in each video. The true detections and the data association across videos is estimated using robust multi-view triangulation and subsequently refined during our bundle adjustment procedure. Quantitative evaluation on simulated data and experiments on real videos from indoor and outdoor scenes demonstrates the effectiveness of our method

Trajectory based video analysis in multi-camera setups

PhDThis thesis presents an automated framework for activity analysis in multi-camera setups. We start with the calibration of cameras particularly without overlapping views. An algorithm is presented that exploits trajectory observations in each view and works iteratively on camera pairs. First outliers are identified and removed from observations of each camera. Next, spatio-temporal information derived from the available trajectory is used to estimate unobserved trajectory segments in areas uncovered by the cameras. The unobserved trajectory estimates are used to estimate the relative position of each camera pair, whereas the exit-entrance direction of each object is used to estimate their relative orientation. The process continues and iteratively approximates the configuration of all cameras with respect to each other. Finally, we refi ne the initial configuration estimates with bundle adjustment, based on the observed and estimated trajectory segments. For cameras with overlapping views, state-of-the-art homography based approaches are used for calibration. Next we establish object correspondence across multiple views. Our algorithm consists of three steps, namely association, fusion and linkage. For association, local trajectory pairs corresponding to the same physical object are estimated using multiple spatio-temporal features on a common ground plane. To disambiguate spurious associations, we employ a hybrid approach that utilises the matching results on the image plane and ground plane. The trajectory segments after association are fused by adaptive averaging. Trajectory linkage then integrates segments and generates a single trajectory of an object across the entire observed area. Finally, for activities analysis clustering is applied on complete trajectories. Our clustering algorithm is based on four main steps, namely the extraction of a set of representative trajectory features, non-parametric clustering, cluster merging and information fusion for the identification of normal and rare object motion patterns. First we transform the trajectories into a set of feature spaces on which Meanshift identi es the modes and the corresponding clusters. Furthermore, a merging procedure is devised to re fine these results by combining similar adjacent clusters. The fi nal common patterns are estimated by fusing the clustering results across all feature spaces. Clusters corresponding to reoccurring trajectories are considered as normal, whereas sparse trajectories are associated to abnormal and rare events. The performance of the proposed framework is evaluated on standard data-sets and compared with state-of-the-art techniques. Experimental results show that the proposed framework outperforms state-of-the-art algorithms both in terms of accuracy and robustness

Algorithms for trajectory integration in multiple views

PhDThis thesis addresses the problem of deriving a coherent and accurate localization of moving objects from partial visual information when data are generated by cameras placed in di erent view angles with respect to the scene. The framework is built around applications of scene monitoring with multiple cameras. Firstly, we demonstrate how a geometric-based solution exploits the relationships between corresponding feature points across views and improves accuracy in object location. Then, we improve the estimation of objects location with geometric transformations that account for lens distortions. Additionally, we study the integration of the partial visual information generated by each individual sensor and their combination into one single frame of observation that considers object association and data fusion. Our approach is fully image-based, only relies on 2D constructs and does not require any complex computation in 3D space. We exploit the continuity and coherence in objects' motion when crossing cameras' elds of view. Additionally, we work under the assumption of planar ground plane and wide baseline (i.e. cameras' viewpoints are far apart). The main contributions are: i) the development of a framework for distributed visual sensing that accounts for inaccuracies in the geometry of multiple views; ii) the reduction of trajectory mapping errors using a statistical-based homography estimation; iii) the integration of a polynomial method for correcting inaccuracies caused by the cameras' lens distortion; iv) a global trajectory reconstruction algorithm that associates and integrates fragments of trajectories generated by each camera

Multitarget Tracking in Nonoverlapping Cameras Using a Reference Set

Tracking multiple targets in nonoverlapping cameras are challenging since the observations of the same targets are often separated by time and space. There might be significant appearance change of a target across camera views caused by variations in illumination conditions, poses, and camera imaging characteristics. Consequently, the same target may appear very different in two cameras. Therefore, associating tracks in different camera views directly based on their appearance similarity is difficult and prone to error. In most previous methods, the appearance similarity is computed either using color histograms or based on pretrained brightness transfer function that maps color between cameras. In this paper, a novel reference set based appearance model is proposed to improve multitarget tracking in a network of nonoverlapping cameras. Contrary to previous work, a reference set is constructed for a pair of cameras, containing subjects appearing in both camera views. For track association, instead of directly comparing the appearance of two targets in different camera views, they are compared indirectly via the reference set. Besides global color histograms, texture and shape features are extracted at different locations of a target, and AdaBoost is used to learn the discriminative power of each feature. The effectiveness of the proposed method over the state of the art on two challenging real-world multicamera video data sets is demonstrated by thorough experiments